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SeekOut

by SeekOut

Open in ChatGPT

Overview

Search SeekOut's talent database, inspect candidate profiles, compare talent markets, discover companies, and manage recruiting workflows from ChatGPT.

Tools

add_filter

ChatGPT
Add a filter to the accumulated session state. Merges with existing — does not replace. type: "titles" | "companies" | "locations" | "schools" | "country" | "state" | "excluded_companies" | "excluded_titles" | "exclude_sources" | "exclude_profile_keys" | "exclude_search_query" | "years_experience_min" | "years_experience_max" | "company_size_min" | "company_size_max" | "specialization" | "has_clearance" | "open_to_remote" | "power_filters" For public-profile searches, use years_experience_min/max for seniority-style constraints rather than a seniority filter. value: ONE entity name per call. Call multiple times for multiple values. For ranges — integer string: add_filter("years_experience_min", "5") For current exclusions — entity name or CSV: add_filter("excluded_companies", "Microsoft, Google") For MCP-side result exclusions: add_filter("exclude_sources", "ats,internal") add_filter("exclude_profile_keys", "profile-key-1,profile-key-2") add_filter("exclude_search_query", "https://app.seekout.io/app?search_id=...") These are not Runtime entity filters and are stored under session["exclusions"]. For the typed clearance wrapper — truthy string: add_filter("has_clearance", "true") For remote availability — truthy string: add_filter("open_to_remote", "true") For generic power filters — filter id string from browse_power_filters: add_filter("power_filters", "general.authorizations.security_clearance") Typed power-filter wrapper (public vertical only) — one call, one runtime power filter: has_clearance — candidates who list a security clearance on their profile open_to_remote — candidates open to remote work Entity names are RESOLVED IMMEDIATELY against SeekOut's database. Check resolution feedback — if names didn't resolve, try alternative spellings. For titles: validates level and function against query_context. Server BLOCKS mismatches. Location hierarchy: US: add_filter("state", "California") for states, add_filter("locations", "Nashville") for cities. Non-US: FIRST set_index("Europe"), THEN add_filter("country", "Sweden"), optionally add_filter("locations", "Stockholm").

add_filter

ChatGPT
Add a filter to the accumulated session state. Merges with existing — does not replace. type: "titles" | "companies" | "locations" | "schools" | "country" | "state" | "excluded_companies" | "excluded_titles" | "exclude_sources" | "exclude_profile_keys" | "exclude_search_query" | "years_experience_min" | "years_experience_max" | "company_size_min" | "company_size_max" | "specialization" | "has_clearance" | "open_to_remote" | "power_filters" For public-profile searches, use years_experience_min/max for seniority-style constraints rather than a seniority filter. value: ONE entity name per call. Call multiple times for multiple values. For ranges — integer string: add_filter("years_experience_min", "5") For current exclusions — entity name or CSV: add_filter("excluded_companies", "Microsoft, Google") For MCP-side result exclusions: add_filter("exclude_sources", "ats,internal") add_filter("exclude_profile_keys", "profile-key-1,profile-key-2") add_filter("exclude_search_query", "https://app.seekout.io/app?search_id=...") These are not Runtime entity filters and are stored under session["exclusions"]. For the typed clearance wrapper — truthy string: add_filter("has_clearance", "true") For remote availability — truthy string: add_filter("open_to_remote", "true") For generic power filters — filter id string from browse_power_filters: add_filter("power_filters", "general.authorizations.security_clearance") Typed power-filter wrapper (public vertical only) — one call, one runtime power filter: has_clearance — candidates who list a security clearance on their profile open_to_remote — candidates open to remote work Entity names are RESOLVED IMMEDIATELY against SeekOut's database. Check resolution feedback — if names didn't resolve, try alternative spellings. For titles: validates level and function against query_context. Server BLOCKS mismatches. Location hierarchy: US: add_filter("state", "California") for states, add_filter("locations", "Nashville") for cities. Non-US: FIRST set_index("Europe"), THEN add_filter("country", "Sweden"), optionally add_filter("locations", "Stockholm").

add_filter

ChatGPT
Add a filter to the accumulated session state. Merges with existing — does not replace. type: "titles" | "companies" | "locations" | "schools" | "country" | "state" | "excluded_companies" | "excluded_titles" | "exclude_sources" | "exclude_profile_keys" | "exclude_search_query" | "years_experience_min" | "years_experience_max" | "company_size_min" | "company_size_max" | "specialization" | "has_clearance" | "open_to_remote" | "power_filters" For public-profile searches, use years_experience_min/max for seniority-style constraints rather than a seniority filter. value: ONE entity name per call. Call multiple times for multiple values. For ranges — integer string: add_filter("years_experience_min", "5") For current exclusions — entity name or CSV: add_filter("excluded_companies", "Microsoft, Google") For MCP-side result exclusions: add_filter("exclude_sources", "ats,internal") add_filter("exclude_profile_keys", "profile-key-1,profile-key-2") add_filter("exclude_search_query", "https://app.seekout.io/app?search_id=...") These are not Runtime entity filters and are stored under session["exclusions"]. For the typed clearance wrapper — truthy string: add_filter("has_clearance", "true") For remote availability — truthy string: add_filter("open_to_remote", "true") For generic power filters — filter id string from browse_power_filters: add_filter("power_filters", "general.authorizations.security_clearance") Typed power-filter wrapper (public vertical only) — one call, one runtime power filter: has_clearance — candidates who list a security clearance on their profile open_to_remote — candidates open to remote work Entity names are RESOLVED IMMEDIATELY against SeekOut's database. Check resolution feedback — if names didn't resolve, try alternative spellings. For titles: validates level and function against query_context. Server BLOCKS mismatches. Location hierarchy: US: add_filter("state", "California") for states, add_filter("locations", "Nashville") for cities. Non-US: FIRST set_index("Europe"), THEN add_filter("country", "Sweden"), optionally add_filter("locations", "Stockholm").

add_search_to_workspace

ChatGPT
Save the current session's search as an additional Saved search on a workspace. Unlike `update_workspace_search (which replaces the workspace's single Base search), this tool POSTs a new Saved search alongside any existing Saved searches and the Base. Use this when the user wants to bookmark multiple distinct candidate populations under one workspace — for example, "senior PMs", "staff PMs", and "GitHub-active PMs" all under one "PM intake" workspace; or a public-profile search alongside a GitHub search. Vertical-aware: stores the active session's Runtime query in query-store, then saves the same route-shaped {pathname, search}` payload recruit-ui creates for manual workspace Saved searches. This lets the Saved search rehydrate into the correct vertical view when clicked. Out-of-scope verticals raise an error rather than silently saving a public-profile-shaped query. Returns the new search id and a link to open the workspace. Args: workspace_id: ID of the workspace to add the Saved search to. name: Human-readable name for this Saved search (shown in the "Saved searches" panel on the workspace). Required — pick a short label that describes the candidate population (e.g., "Senior PMs in NYC", "Rust devs with 100+ stars"). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

add_search_to_workspace

ChatGPT
Save the current session's search as an additional Saved search on a workspace. Unlike `update_workspace_search (which replaces the workspace's single Base search), this tool POSTs a new Saved search alongside any existing Saved searches and the Base. Use this when the user wants to bookmark multiple distinct candidate populations under one workspace — for example, "senior PMs", "staff PMs", and "GitHub-active PMs" all under one "PM intake" workspace; or a public-profile search alongside a GitHub search. Vertical-aware: stores the active session's Runtime query in query-store, then saves the same route-shaped {pathname, search}` payload recruit-ui creates for manual workspace Saved searches. This lets the Saved search rehydrate into the correct vertical view when clicked. Out-of-scope verticals raise an error rather than silently saving a public-profile-shaped query. Returns the new search id and a link to open the workspace. Args: workspace_id: ID of the workspace to add the Saved search to. name: Human-readable name for this Saved search (shown in the "Saved searches" panel on the workspace). Required — pick a short label that describes the candidate population (e.g., "Senior PMs in NYC", "Rust devs with 100+ stars"). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

add_search_to_workspace

ChatGPT
Save the current session's search as an additional Saved search on a workspace. Unlike `update_workspace_search (which replaces the workspace's single Base search), this tool POSTs a new Saved search alongside any existing Saved searches and the Base. Use this when the user wants to bookmark multiple distinct candidate populations under one workspace — for example, "senior PMs", "staff PMs", and "GitHub-active PMs" all under one "PM intake" workspace; or a public-profile search alongside a GitHub search. Vertical-aware: stores the active session's Runtime query in query-store, then saves the same route-shaped {pathname, search}` payload recruit-ui creates for manual workspace Saved searches. This lets the Saved search rehydrate into the correct vertical view when clicked. Out-of-scope verticals raise an error rather than silently saving a public-profile-shaped query. Returns the new search id and a link to open the workspace. Args: workspace_id: ID of the workspace to add the Saved search to. name: Human-readable name for this Saved search (shown in the "Saved searches" panel on the workspace). Required — pick a short label that describes the candidate population (e.g., "Senior PMs in NYC", "Rust devs with 100+ stars"). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

add_to_workspace

ChatGPT
Add candidate profiles to a workspace. ALWAYS include user_intent (user's exact message) and user_intent_category. Resolves the workspace by name, validates profile keys, and adds profiles. Duplicates are handled gracefully (idempotent merge). Max 100 profiles per call. For bulk adds from the active search, pass from_current_search=True, skip=0, limit=100. To add the next page, call again with the returned batch.next_skip value. Args: workspace_name: Name of the workspace to add profiles to. profile_keys: Candidate profile keys from search results (max 100). from_current_search: When true and profile_keys is empty, add the current search page instead. skip: Current-search offset for paginated bulk adds. limit: Number of current-search candidates to add (max 100). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

add_to_workspace

ChatGPT
Add candidate profiles to a workspace. ALWAYS include user_intent (user's exact message) and user_intent_category. Resolves the workspace by name, validates profile keys, and adds profiles. Duplicates are handled gracefully (idempotent merge). Max 100 profiles per call. For bulk adds from the active search, pass from_current_search=True, skip=0, limit=100. To add the next page, call again with the returned batch.next_skip value. Args: workspace_name: Name of the workspace to add profiles to. profile_keys: Candidate profile keys from search results (max 100). from_current_search: When true and profile_keys is empty, add the current search page instead. skip: Current-search offset for paginated bulk adds. limit: Number of current-search candidates to add (max 100). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

add_to_workspace

ChatGPT
Add candidate profiles to a workspace. ALWAYS include user_intent (user's exact message) and user_intent_category. Resolves the workspace by name, validates profile keys, and adds profiles. Duplicates are handled gracefully (idempotent merge). Max 100 profiles per call. For bulk adds from the active search, pass from_current_search=True, skip=0, limit=100. To add the next page, call again with the returned batch.next_skip value. Args: workspace_name: Name of the workspace to add profiles to. profile_keys: Candidate profile keys from search results (max 100). from_current_search: When true and profile_keys is empty, add the current search page instead. skip: Current-search offset for paginated bulk adds. limit: Number of current-search candidates to add (max 100). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

analyze_bench_depth

ChatGPT
Analyze bench depth for one or more critical roles. For each role: searches the Employee index by title AND by expanded skills (via skill_resolver), merges & dedupes by profile_key, scores skill-overlap percentage against the role's required skills, and returns the top candidates with how each was found. Args: critical_roles: role titles to analyze (e.g., "Principal ML Engineer"). Capped at `max_roles. scope_manager: optional "Name / email" string. Restricts the search to that manager's direct reports (existing manager` filter; profile-key-based subtree filtering isn't supported by runtime — see F1.5 close-out). department: optional department name to scope to. max_roles: cap on the number of roles processed. max_candidates_per_role: cap on top candidates returned per role. use_org_context: opt-in for skill_resolver org-context expansion. Default True for internal_talent.

analyze_bench_depth

ChatGPT
Analyze bench depth for one or more critical roles. For each role: searches the Employee index by title AND by expanded skills (via skill_resolver), merges & dedupes by profile_key, scores skill-overlap percentage against the role's required skills, and returns the top candidates with how each was found. Args: critical_roles: role titles to analyze (e.g., "Principal ML Engineer"). Capped at `max_roles. scope_manager: optional "Name / email" string. Restricts the search to that manager's direct reports (existing manager` filter; profile-key-based subtree filtering isn't supported by runtime — see F1.5 close-out). department: optional department name to scope to. max_roles: cap on the number of roles processed. max_candidates_per_role: cap on top candidates returned per role. use_org_context: opt-in for skill_resolver org-context expansion. Default True for internal_talent.

analyze_bench_depth

ChatGPT
Analyze bench depth for one or more critical roles. For each role: searches the Employee index by title AND by expanded skills (via skill_resolver), merges & dedupes by profile_key, scores skill-overlap percentage against the role's required skills, and returns the top candidates with how each was found. Args: critical_roles: role titles to analyze (e.g., "Principal ML Engineer"). Capped at `max_roles. scope_manager: optional "Name / email" string. Restricts the search to that manager's direct reports (existing manager` filter; profile-key-based subtree filtering isn't supported by runtime — see F1.5 close-out). department: optional department name to scope to. max_roles: cap on the number of roles processed. max_candidates_per_role: cap on top candidates returned per role. use_org_context: opt-in for skill_resolver org-context expansion. Default True for internal_talent.

browse_power_filters

ChatGPT
Browse available power filters for the current search vertical. Returns categories and their power filters. Use without arguments to see all categories. Pass a category name to see its filters. Apply filters using add_filter(type="power_filters", value="filter_id"). Custom power filters created by your organization are included. Use ONLY for these specific scenarios: - Security clearance searches (e.g., TS/SCI, Secret, Top Secret) - Government/defense sector searches - Career level filters for big tech companies (e.g., L5, L6, Principal) - When the user explicitly asks for a power filter or custom filter Do NOT use for general searches — standard filters (titles, companies, locations, skills) are better handled by add_filter.

browse_power_filters

ChatGPT
Browse available power filters for the current search vertical. Returns categories and their power filters. Use without arguments to see all categories. Pass a category name to see its filters. Apply filters using add_filter(type="power_filters", value="filter_id"). Custom power filters created by your organization are included. Use ONLY for these specific scenarios: - Security clearance searches (e.g., TS/SCI, Secret, Top Secret) - Government/defense sector searches - Career level filters for big tech companies (e.g., L5, L6, Principal) - When the user explicitly asks for a power filter or custom filter Do NOT use for general searches — standard filters (titles, companies, locations, skills) are better handled by add_filter.

browse_power_filters

ChatGPT
Browse available power filters for the current search vertical. Returns categories and their power filters. Use without arguments to see all categories. Pass a category name to see its filters. Apply filters using add_filter(type="power_filters", value="filter_id"). Custom power filters created by your organization are included. Use ONLY for these specific scenarios: - Security clearance searches (e.g., TS/SCI, Secret, Top Secret) - Government/defense sector searches - Career level filters for big tech companies (e.g., L5, L6, Principal) - When the user explicitly asks for a power filter or custom filter Do NOT use for general searches — standard filters (titles, companies, locations, skills) are better handled by add_filter.

check_contacts

ChatGPT
Check status of all background contact retrieval jobs. Auto-detects which jobs are running (emails, phones, or both). Returns results for completed jobs and status for in-progress ones. Call get_emails() or get_phones() first to start a retrieval job.

check_contacts

ChatGPT
Check status of all background contact retrieval jobs. Auto-detects which jobs are running (emails, phones, or both). Returns results for completed jobs and status for in-progress ones. Call get_emails() or get_phones() first to start a retrieval job.

check_contacts

ChatGPT
Check status of all background contact retrieval jobs. Auto-detects which jobs are running (emails, phones, or both). Returns results for completed jobs and status for in-progress ones. Call get_emails() or get_phones() first to start a retrieval job.

check_credits

ChatGPT
Check your remaining export and contact credits. Returns a summary of available and used credits for: - Export credits (used when exporting profiles to Excel or ATS) - Contact credits (used when retrieving email addresses) Args: user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

check_credits

ChatGPT
Check your remaining export and contact credits. Returns a summary of available and used credits for: - Export credits (used when exporting profiles to Excel or ATS) - Contact credits (used when retrieving email addresses) Args: user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

check_credits

ChatGPT
Check your remaining export and contact credits. Returns a summary of available and used credits for: - Export credits (used when exporting profiles to Excel or ATS) - Contact credits (used when retrieving email addresses) Args: user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

compare_markets

ChatGPT
Compare talent pools across multiple cities side by side. Runs parallel searches with the same query/filters but different location constraints for each city. Returns per-city pool sizes and facet breakdowns (companies, titles, seniority, skills). Args: cities: List of city names to compare (2-6 cities). Example: ["New York", "Seattle", "San Francisco"] query: Boolean search query with cur_title:() syntax. Example: cur_title:("Software Engineer") titles: Optional comma-separated title filter values. skills: Optional comma-separated skill filter values. seniority: Optional comma-separated seniority filter values. index: Geographic index (default NorthAmerica).

compare_markets

ChatGPT
Compare talent pools across multiple cities side by side. Runs parallel searches with the same query/filters but different location constraints for each city. Returns per-city pool sizes and facet breakdowns (companies, titles, seniority, skills). Args: cities: List of city names to compare (2-6 cities). Example: ["New York", "Seattle", "San Francisco"] query: Boolean search query with cur_title:() syntax. Example: cur_title:("Software Engineer") titles: Optional comma-separated title filter values. skills: Optional comma-separated skill filter values. seniority: Optional comma-separated seniority filter values. index: Geographic index (default NorthAmerica).

compare_markets

ChatGPT
Compare talent pools across multiple cities side by side. Runs parallel searches with the same query/filters but different location constraints for each city. Returns per-city pool sizes and facet breakdowns (companies, titles, seniority, skills). Args: cities: List of city names to compare (2-6 cities). Example: ["New York", "Seattle", "San Francisco"] query: Boolean search query with cur_title:() syntax. Example: cur_title:("Software Engineer") titles: Optional comma-separated title filter values. skills: Optional comma-separated skill filter values. seniority: Optional comma-separated seniority filter values. index: Geographic index (default NorthAmerica).

count_results

ChatGPT
Quick count of candidates matching current session filters. No profiles returned. For public profiles: requires at least one filter or a boolean query (title, location, companies, skills boolean, etc. — any single constraint is enough). Returns {total_count, facets}. For non-public verticals: counts with whatever filters are active. Use to check pool size BEFORE calling search_people. include_facets: Whether to include facet breakdowns in the response (default True).

count_results

ChatGPT
Quick count of candidates matching current session filters. No profiles returned. For public profiles: requires at least one filter or a boolean query (title, location, companies, skills boolean, etc. — any single constraint is enough). Returns {total_count, facets}. For non-public verticals: counts with whatever filters are active. Use to check pool size BEFORE calling search_people. include_facets: Whether to include facet breakdowns in the response (default True).

count_results

ChatGPT
Quick count of candidates matching current session filters. No profiles returned. For public profiles: requires at least one filter or a boolean query (title, location, companies, skills boolean, etc. — any single constraint is enough). Returns {total_count, facets}. For non-public verticals: counts with whatever filters are active. Use to check pool size BEFORE calling search_people. include_facets: Whether to include facet breakdowns in the response (default True).

create_workspace

ChatGPT
Create a new workspace. Behavior depends on from_current_search: - from_current_search=False (default): create a new empty workspace. - from_current_search=True: create a new workspace seeded with your current search (query, filters, index). Always provide the user a link to the workspace. Vertical-aware: when the active session vertical is github, healthcare, nursing, or academic, the workspace's base search is built using that vertical's query shape so recruit-ui rehydrates into the correct vertical view. Out-of-scope verticals (internal_talent, ats, ar, etc.) raise an error rather than silently saving a public-profile-shaped query. Hire-intent EXPRESS pipeline: when from_current_search=True and user_intent_category="hire", the workspace is created with creationType="EXPRESS" so recruit-api asynchronously generates a draft questionnaire and evaluation rubric from the description. Provide a substantive description (at least the target role plus 2 must-haves) for hire-intent flows — this seeds the questionnaire generator. For non-hire intents (research, competitive_intel, market_sizing, outreach) the workspace is created with creationType="QUERY" — base search saved, no questionnaire generated. Returns the workspace id, name, and a link to open it in SeekOut. Args: name: Name for the new workspace. description: Hiring-manager intent / role headline. Strongly recommended for hire-intent workspaces (seeds the EXPRESS questionnaire generator). Capture target role, must-have skills, nice-to-haves, level, location preferences in 2-4 sentences. Long descriptions are truncated at sentence boundaries; thin (<50 char) descriptions on hire intent are logged but not blocked. from_current_search: When True, seed the workspace with the current search session (query, filters, index). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other. When "hire" combined with from_current_search=True, the workspace uses EXPRESS creation type to populate questionnaire + rubric.

create_workspace

ChatGPT
Create a new workspace. Behavior depends on from_current_search: - from_current_search=False (default): create a new empty workspace. - from_current_search=True: create a new workspace seeded with your current search (query, filters, index). Always provide the user a link to the workspace. Vertical-aware: when the active session vertical is github, healthcare, nursing, or academic, the workspace's base search is built using that vertical's query shape so recruit-ui rehydrates into the correct vertical view. Out-of-scope verticals (internal_talent, ats, ar, etc.) raise an error rather than silently saving a public-profile-shaped query. Hire-intent EXPRESS pipeline: when from_current_search=True and user_intent_category="hire", the workspace is created with creationType="EXPRESS" so recruit-api asynchronously generates a draft questionnaire and evaluation rubric from the description. Provide a substantive description (at least the target role plus 2 must-haves) for hire-intent flows — this seeds the questionnaire generator. For non-hire intents (research, competitive_intel, market_sizing, outreach) the workspace is created with creationType="QUERY" — base search saved, no questionnaire generated. Returns the workspace id, name, and a link to open it in SeekOut. Args: name: Name for the new workspace. description: Hiring-manager intent / role headline. Strongly recommended for hire-intent workspaces (seeds the EXPRESS questionnaire generator). Capture target role, must-have skills, nice-to-haves, level, location preferences in 2-4 sentences. Long descriptions are truncated at sentence boundaries; thin (<50 char) descriptions on hire intent are logged but not blocked. from_current_search: When True, seed the workspace with the current search session (query, filters, index). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other. When "hire" combined with from_current_search=True, the workspace uses EXPRESS creation type to populate questionnaire + rubric.

create_workspace

ChatGPT
Create a new workspace. Behavior depends on from_current_search: - from_current_search=False (default): create a new empty workspace. - from_current_search=True: create a new workspace seeded with your current search (query, filters, index). Always provide the user a link to the workspace. Vertical-aware: when the active session vertical is github, healthcare, nursing, or academic, the workspace's base search is built using that vertical's query shape so recruit-ui rehydrates into the correct vertical view. Out-of-scope verticals (internal_talent, ats, ar, etc.) raise an error rather than silently saving a public-profile-shaped query. Hire-intent EXPRESS pipeline: when from_current_search=True and user_intent_category="hire", the workspace is created with creationType="EXPRESS" so recruit-api asynchronously generates a draft questionnaire and evaluation rubric from the description. Provide a substantive description (at least the target role plus 2 must-haves) for hire-intent flows — this seeds the questionnaire generator. For non-hire intents (research, competitive_intel, market_sizing, outreach) the workspace is created with creationType="QUERY" — base search saved, no questionnaire generated. Returns the workspace id, name, and a link to open it in SeekOut. Args: name: Name for the new workspace. description: Hiring-manager intent / role headline. Strongly recommended for hire-intent workspaces (seeds the EXPRESS questionnaire generator). Capture target role, must-have skills, nice-to-haves, level, location preferences in 2-4 sentences. Long descriptions are truncated at sentence boundaries; thin (<50 char) descriptions on hire intent are logged but not blocked. from_current_search: When True, seed the workspace with the current search session (query, filters, index). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other. When "hire" combined with from_current_search=True, the workspace uses EXPRESS creation type to populate questionnaire + rubric.

describe_org_schema

ChatGPT
Describe the Employee-index schema for a single org. Call ONCE at the start of an internal_talent session, then rely on the returned vocabularies + `configurable_data_fields to decide which filters and skill names are usable for this tenant. Returns JSON with: - available_filters: filter names + descriptions - skill_facets / department_vocab / title_vocab / seniority_vocab / office_location_vocab: per-tenant real vocabularies - configurable_data_fields: union of CD keys observed across the sample, with per-key occupancy in [0, 1] - tenant_shape: populated-rate signals for top-level fields (skills / department / manager / hierarchy / internal_skills) - total_employees, sample_size, as_of` Args: org_domain: Org domain (e.g., 'hooli.com'). Defaults to the authenticated org if omitted. force_refresh: Bypass the 1h cache. Use sparingly — the schema only changes when the org adds/removes configurable_data fields or changes index settings, which is rare.

describe_org_schema

ChatGPT
Describe the Employee-index schema for a single org. Call ONCE at the start of an internal_talent session, then rely on the returned vocabularies + `configurable_data_fields to decide which filters and skill names are usable for this tenant. Returns JSON with: - available_filters: filter names + descriptions - skill_facets / department_vocab / title_vocab / seniority_vocab / office_location_vocab: per-tenant real vocabularies - configurable_data_fields: union of CD keys observed across the sample, with per-key occupancy in [0, 1] - tenant_shape: populated-rate signals for top-level fields (skills / department / manager / hierarchy / internal_skills) - total_employees, sample_size, as_of` Args: org_domain: Org domain (e.g., 'hooli.com'). Defaults to the authenticated org if omitted. force_refresh: Bypass the 1h cache. Use sparingly — the schema only changes when the org adds/removes configurable_data fields or changes index settings, which is rare.

describe_org_schema

ChatGPT
Describe the Employee-index schema for a single org. Call ONCE at the start of an internal_talent session, then rely on the returned vocabularies + `configurable_data_fields to decide which filters and skill names are usable for this tenant. Returns JSON with: - available_filters: filter names + descriptions - skill_facets / department_vocab / title_vocab / seniority_vocab / office_location_vocab: per-tenant real vocabularies - configurable_data_fields: union of CD keys observed across the sample, with per-key occupancy in [0, 1] - tenant_shape: populated-rate signals for top-level fields (skills / department / manager / hierarchy / internal_skills) - total_employees, sample_size, as_of` Args: org_domain: Org domain (e.g., 'hooli.com'). Defaults to the authenticated org if omitted. force_refresh: Bypass the 1h cache. Use sparingly — the schema only changes when the org adds/removes configurable_data fields or changes index settings, which is rare.

discover_companies

ChatGPT
Find companies in an industry using AI generation + SeekOut database verification. Uses an LLM to generate 50 companies in the industry, then verifies each against SeekOut's entity database. Returns only verified companies ready for add_filter. Use for industry/sector queries: "at biotech", "in cybersecurity", "gaming companies", "unicorn", "series b startups". Then add results: add_filter("companies", "Company1, Company2, ...") industry: Industry keyword (e.g. "biotech", "cybersecurity", "gaming", "fintech") location: Optional geographic hint (e.g. "Texas", "UK")

discover_companies

ChatGPT
Find companies in an industry using AI generation + SeekOut database verification. Uses an LLM to generate 50 companies in the industry, then verifies each against SeekOut's entity database. Returns only verified companies ready for add_filter. Use for industry/sector queries: "at biotech", "in cybersecurity", "gaming companies", "unicorn", "series b startups". Then add results: add_filter("companies", "Company1, Company2, ...") industry: Industry keyword (e.g. "biotech", "cybersecurity", "gaming", "fintech") location: Optional geographic hint (e.g. "Texas", "UK")

discover_companies

ChatGPT
Find companies in an industry using AI generation + SeekOut database verification. Uses an LLM to generate 50 companies in the industry, then verifies each against SeekOut's entity database. Returns only verified companies ready for add_filter. Use for industry/sector queries: "at biotech", "in cybersecurity", "gaming companies", "unicorn", "series b startups". Then add results: add_filter("companies", "Company1, Company2, ...") industry: Industry keyword (e.g. "biotech", "cybersecurity", "gaming", "fintech") location: Optional geographic hint (e.g. "Texas", "UK")

execute_app_action

ChatGPT
Execute a UI-triggered app action through the audited action executor.

execute_app_action

ChatGPT
Execute a UI-triggered app action through the audited action executor.

execute_app_action

ChatGPT
Execute a UI-triggered app action through the audited action executor.

export_profiles

ChatGPT
Export candidate profiles to Excel or Google Sheets. ALWAYS include user_intent (user's exact message) and user_intent_category. Returns a download URL for the exported file. Supported formats: 'excel', 'sheets' (both produce an Excel file). For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys to export. format: Export format — 'excel' or 'sheets' (both use Excel format). project_name: Optional name for the export project. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

export_profiles

ChatGPT
Export candidate profiles to Excel or Google Sheets. ALWAYS include user_intent (user's exact message) and user_intent_category. Returns a download URL for the exported file. Supported formats: 'excel', 'sheets' (both produce an Excel file). For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys to export. format: Export format — 'excel' or 'sheets' (both use Excel format). project_name: Optional name for the export project. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

export_profiles

ChatGPT
Export candidate profiles to Excel or Google Sheets. ALWAYS include user_intent (user's exact message) and user_intent_category. Returns a download URL for the exported file. Supported formats: 'excel', 'sheets' (both produce an Excel file). For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys to export. format: Export format — 'excel' or 'sheets' (both use Excel format). project_name: Optional name for the export project. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

export_to_ats

ChatGPT
Export candidate profiles to your ATS (Applicant Tracking System). ALWAYS include user_intent (user's exact message) and user_intent_category. The ATS connection is auto-detected from your account. If you have multiple export-capable ATS connections, specify which one to use or call list_ats_connections first. Args: profile_keys: List of candidate profile keys to export. ats_connection_id: Optional ATS connection ID. Auto-resolved if omitted. project_name: Optional name for the export project. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

export_to_ats

ChatGPT
Export candidate profiles to your ATS (Applicant Tracking System). ALWAYS include user_intent (user's exact message) and user_intent_category. The ATS connection is auto-detected from your account. If you have multiple export-capable ATS connections, specify which one to use or call list_ats_connections first. Args: profile_keys: List of candidate profile keys to export. ats_connection_id: Optional ATS connection ID. Auto-resolved if omitted. project_name: Optional name for the export project. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

export_to_ats

ChatGPT
Export candidate profiles to your ATS (Applicant Tracking System). ALWAYS include user_intent (user's exact message) and user_intent_category. The ATS connection is auto-detected from your account. If you have multiple export-capable ATS connections, specify which one to use or call list_ats_connections first. Args: profile_keys: List of candidate profile keys to export. ats_connection_id: Optional ATS connection ID. Auto-resolved if omitted. project_name: Optional name for the export project. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_emails

ChatGPT
Start email retrieval for candidate profiles. ALWAYS include user_intent (user's exact message) and user_intent_category. Kicks off a background job to retrieve ranked email addresses. Use check_contacts() to get results when ready. Results may be returned immediately if contacts are already cached. For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys from search results. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_emails

ChatGPT
Start email retrieval for candidate profiles. ALWAYS include user_intent (user's exact message) and user_intent_category. Kicks off a background job to retrieve ranked email addresses. Use check_contacts() to get results when ready. Results may be returned immediately if contacts are already cached. For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys from search results. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_emails

ChatGPT
Start email retrieval for candidate profiles. ALWAYS include user_intent (user's exact message) and user_intent_category. Kicks off a background job to retrieve ranked email addresses. Use check_contacts() to get results when ready. Results may be returned immediately if contacts are already cached. For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys from search results. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_phones

ChatGPT
Start phone number retrieval for candidate profiles. ALWAYS include user_intent (user's exact message) and user_intent_category. Kicks off a background job to retrieve phone numbers. Use check_contacts() to get results when ready. Results may be returned immediately if contacts are already cached. For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys from search results. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_phones

ChatGPT
Start phone number retrieval for candidate profiles. ALWAYS include user_intent (user's exact message) and user_intent_category. Kicks off a background job to retrieve phone numbers. Use check_contacts() to get results when ready. Results may be returned immediately if contacts are already cached. For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys from search results. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_phones

ChatGPT
Start phone number retrieval for candidate profiles. ALWAYS include user_intent (user's exact message) and user_intent_category. Kicks off a background job to retrieve phone numbers. Use check_contacts() to get results when ready. Results may be returned immediately if contacts are already cached. For batches over 25 profiles, returns a confirmation prompt with credit impact details. Call again with confirm_large_batch=True after user confirms. Args: profile_keys: List of candidate profile keys from search results. confirm_large_batch: Set to True to confirm a batch over 25 profiles. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_profile

ChatGPT
Fetch full candidate profiles by profile key(s) from search_people results. keys: CSV string of profile_key values (max 15). Returns unified profiles with data from all available verticals — public LinkedIn profile, ATS applications, internal talent data — merged into one response. Keys from different verticals (e.g., a GitHub key + a public profile key) are automatically de-duplicated — the same person appears once with data from all verticals.

get_profile

ChatGPT
Fetch full candidate profiles by profile key(s) from search_people results. keys: CSV string of profile_key values (max 15). Returns unified profiles with data from all available verticals — public LinkedIn profile, ATS applications, internal talent data — merged into one response. Keys from different verticals (e.g., a GitHub key + a public profile key) are automatically de-duplicated — the same person appears once with data from all verticals.

get_profile

ChatGPT
Fetch full candidate profiles by profile key(s) from search_people results. keys: CSV string of profile_key values (max 15). Returns unified profiles with data from all available verticals — public LinkedIn profile, ATS applications, internal talent data — merged into one response. Keys from different verticals (e.g., a GitHub key + a public profile key) are automatically de-duplicated — the same person appears once with data from all verticals.

get_state

ChatGPT
Return accumulated filters, query, index, and (optionally) the runtime query. Default mode returns the session state as JSON: filters, the boolean query text, the active geographic index, the active vertical, and any ATS-specific context. Use this to inspect the session before searching or to debug filter accumulation. When `include_runtime_query=True (or store=True), the response also includes runtime_query — the exact dict that would be POSTed to the active vertical's Runtime API endpoint — plus endpoint. Use that for bulk operations like SearchPeopleKeys that bypass the MCP's result cap. When store=True, also persists the query via the query store and returns a shareable SeekOut app link in the link field. Args: include_runtime_query: When True, include the runtime API payload under runtime_query and the target endpoint under endpoint. top: Maximum results in the runtime query (Runtime accepts higher than the MCP's 25 cap). Only meaningful with include_runtime_query or store. skip: Pagination offset for the runtime query. include_facets: Whether to include facet requests in the runtime query. store: When True, store the runtime query and return a shareable SeekOut app link (implies include_runtime_query`). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_state

ChatGPT
Return accumulated filters, query, index, and (optionally) the runtime query. Default mode returns the session state as JSON: filters, the boolean query text, the active geographic index, the active vertical, and any ATS-specific context. Use this to inspect the session before searching or to debug filter accumulation. When `include_runtime_query=True (or store=True), the response also includes runtime_query — the exact dict that would be POSTed to the active vertical's Runtime API endpoint — plus endpoint. Use that for bulk operations like SearchPeopleKeys that bypass the MCP's result cap. When store=True, also persists the query via the query store and returns a shareable SeekOut app link in the link field. Args: include_runtime_query: When True, include the runtime API payload under runtime_query and the target endpoint under endpoint. top: Maximum results in the runtime query (Runtime accepts higher than the MCP's 25 cap). Only meaningful with include_runtime_query or store. skip: Pagination offset for the runtime query. include_facets: Whether to include facet requests in the runtime query. store: When True, store the runtime query and return a shareable SeekOut app link (implies include_runtime_query`). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

get_state

ChatGPT
Return accumulated filters, query, index, and (optionally) the runtime query. Default mode returns the session state as JSON: filters, the boolean query text, the active geographic index, the active vertical, and any ATS-specific context. Use this to inspect the session before searching or to debug filter accumulation. When `include_runtime_query=True (or store=True), the response also includes runtime_query — the exact dict that would be POSTed to the active vertical's Runtime API endpoint — plus endpoint. Use that for bulk operations like SearchPeopleKeys that bypass the MCP's result cap. When store=True, also persists the query via the query store and returns a shareable SeekOut app link in the link field. Args: include_runtime_query: When True, include the runtime API payload under runtime_query and the target endpoint under endpoint. top: Maximum results in the runtime query (Runtime accepts higher than the MCP's 25 cap). Only meaningful with include_runtime_query or store. skip: Pagination offset for the runtime query. include_facets: Whether to include facet requests in the runtime query. store: When True, store the runtime query and return a shareable SeekOut app link (implies include_runtime_query`). user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

list_ats_connections

ChatGPT
List available ATS connections for your organization. Returns the ATS integrations configured for your account, including connection ID, name, ATS type, and supported features. Use the connection ID with export_to_ats to export candidates to a specific ATS. Args: feature_filter: Optional feature to filter by. Valid values: 'export', 'inbound', 'analytics', 'connect', 'rediscovery'. When set, only connections with that feature are returned. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

list_ats_connections

ChatGPT
List available ATS connections for your organization. Returns the ATS integrations configured for your account, including connection ID, name, ATS type, and supported features. Use the connection ID with export_to_ats to export candidates to a specific ATS. Args: feature_filter: Optional feature to filter by. Valid values: 'export', 'inbound', 'analytics', 'connect', 'rediscovery'. When set, only connections with that feature are returned. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

list_ats_connections

ChatGPT
List available ATS connections for your organization. Returns the ATS integrations configured for your account, including connection ID, name, ATS type, and supported features. Use the connection ID with export_to_ats to export candidates to a specific ATS. Args: feature_filter: Optional feature to filter by. Valid values: 'export', 'inbound', 'analytics', 'connect', 'rediscovery'. When set, only connections with that feature are returned. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

list_resources

ChatGPT
List all available resources and resource templates. Returns JSON with resource metadata. Static resources have a 'uri' field, while templates have a 'uri_template' field with placeholders like {name}.

list_resources

ChatGPT
List all available resources and resource templates. Returns JSON with resource metadata. Static resources have a 'uri' field, while templates have a 'uri_template' field with placeholders like {name}.

list_resources

ChatGPT
List all available resources and resource templates. Returns JSON with resource metadata. Static resources have a 'uri' field, while templates have a 'uri_template' field with placeholders like {name}.

list_workspaces

ChatGPT
List your workspaces. Optionally filter by name. Returns workspace id, name, profile count, creation date, and a link to open each workspace in SeekOut. Use this to find a workspace before adding profiles to it.

list_workspaces

ChatGPT
List your workspaces. Optionally filter by name. Returns workspace id, name, profile count, creation date, and a link to open each workspace in SeekOut. Use this to find a workspace before adding profiles to it.

list_workspaces

ChatGPT
List your workspaces. Optionally filter by name. Returns workspace id, name, profile count, creation date, and a link to open each workspace in SeekOut. Use this to find a workspace before adding profiles to it.

market_analysis

ChatGPT
Analyze the talent market for your current search criteria. Uses current session state (query, filters, index, vertical) to compute pool sizes and facet distributions scoped to your current search context. Args: expand_regions: If True and searching public profiles, also show pool sizes for all 6 geographic regions. Default False — only analyzes the current index/vertical. include_region_facets: If True (requires expand_regions), include per-region facet distributions (companies, titles, skills by region). Can be verbose — use only when comparing regions. Returns: JSON with pool_size, distributions, and optionally by_region + distributions_by_region.

market_analysis

ChatGPT
Analyze the talent market for your current search criteria. Uses current session state (query, filters, index, vertical) to compute pool sizes and facet distributions scoped to your current search context. Args: expand_regions: If True and searching public profiles, also show pool sizes for all 6 geographic regions. Default False — only analyzes the current index/vertical. include_region_facets: If True (requires expand_regions), include per-region facet distributions (companies, titles, skills by region). Can be verbose — use only when comparing regions. Returns: JSON with pool_size, distributions, and optionally by_region + distributions_by_region.

market_analysis

ChatGPT
Analyze the talent market for your current search criteria. Uses current session state (query, filters, index, vertical) to compute pool sizes and facet distributions scoped to your current search context. Args: expand_regions: If True and searching public profiles, also show pool sizes for all 6 geographic regions. Default False — only analyzes the current index/vertical. include_region_facets: If True (requires expand_regions), include per-region facet distributions (companies, titles, skills by region). Can be verbose — use only when comparing regions. Returns: JSON with pool_size, distributions, and optionally by_region + distributions_by_region.

pipeline_overview

ChatGPT
Sample-derived ATS pipeline analytics in one call. Use when the user asks "give me a pipeline overview", "where are candidates by stage?", "who's running the most reqs?", or anything that wants a one-shot summary of the active pipeline state. Saves 14-18 LLM turns vs composing per-status / per-recruiter count_results calls. Args: connection_id: Optional ATS connection ID. Defaults to the session's resolved connection (set when the user first searched ATS). req_filter: Optional job_id or requisition_id to scope to one req. Empty = all reqs in the connection's pipeline. time_range: Optional ISO-date range like "2024-01-01..2024-12-31". Empty = all time. (Currently informational — backend application filter does not yet expose applied_at range here.) breakdowns: Subset of {"status", "stage", "recruiter", "rejection_reason", "timeline"}. None or empty = all of them. Returns: { "connection_id": str, "ats_type": str, "sample_size": int, "status_breakdown": {...}, "pipeline_stages": {...}, "recruiters": {...}, "rejection_reasons": {...}, "application_timeline": {...}, "data_completeness_warning": {"notes": [...], "cache_hit": bool} } Notes: - source breakdown is intentionally out of scope for v1 — the field isn't in the response. - True time-in-stage transitions are not available; only current-stage breakdown is computed.

pipeline_overview

ChatGPT
Sample-derived ATS pipeline analytics in one call. Use when the user asks "give me a pipeline overview", "where are candidates by stage?", "who's running the most reqs?", or anything that wants a one-shot summary of the active pipeline state. Saves 14-18 LLM turns vs composing per-status / per-recruiter count_results calls. Args: connection_id: Optional ATS connection ID. Defaults to the session's resolved connection (set when the user first searched ATS). req_filter: Optional job_id or requisition_id to scope to one req. Empty = all reqs in the connection's pipeline. time_range: Optional ISO-date range like "2024-01-01..2024-12-31". Empty = all time. (Currently informational — backend application filter does not yet expose applied_at range here.) breakdowns: Subset of {"status", "stage", "recruiter", "rejection_reason", "timeline"}. None or empty = all of them. Returns: { "connection_id": str, "ats_type": str, "sample_size": int, "status_breakdown": {...}, "pipeline_stages": {...}, "recruiters": {...}, "rejection_reasons": {...}, "application_timeline": {...}, "data_completeness_warning": {"notes": [...], "cache_hit": bool} } Notes: - source breakdown is intentionally out of scope for v1 — the field isn't in the response. - True time-in-stage transitions are not available; only current-stage breakdown is computed.

pipeline_overview

ChatGPT
Sample-derived ATS pipeline analytics in one call. Use when the user asks "give me a pipeline overview", "where are candidates by stage?", "who's running the most reqs?", or anything that wants a one-shot summary of the active pipeline state. Saves 14-18 LLM turns vs composing per-status / per-recruiter count_results calls. Args: connection_id: Optional ATS connection ID. Defaults to the session's resolved connection (set when the user first searched ATS). req_filter: Optional job_id or requisition_id to scope to one req. Empty = all reqs in the connection's pipeline. time_range: Optional ISO-date range like "2024-01-01..2024-12-31". Empty = all time. (Currently informational — backend application filter does not yet expose applied_at range here.) breakdowns: Subset of {"status", "stage", "recruiter", "rejection_reason", "timeline"}. None or empty = all of them. Returns: { "connection_id": str, "ats_type": str, "sample_size": int, "status_breakdown": {...}, "pipeline_stages": {...}, "recruiters": {...}, "rejection_reasons": {...}, "application_timeline": {...}, "data_completeness_warning": {"notes": [...], "cache_hit": bool} } Notes: - source breakdown is intentionally out of scope for v1 — the field isn't in the response. - True time-in-stage transitions are not available; only current-stage breakdown is computed.

read_resource

ChatGPT
Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.

read_resource

ChatGPT
Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.

read_resource

ChatGPT
Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.

remove_filter

ChatGPT
Remove a filter value, or clear all values for a field. To remove a specific value: remove_filter("companies", "Google") To clear entire field: remove_filter("companies") If the user's most recent message doesn't look like a drop instruction (e.g. "focus on must-haves" is a priority hint, not a drop), the server attaches an `advisory field to the response. The removal still proceeds — the advisory is informational only. Critical-filter anchor (non-public verticals only): each vertical declares a set of filter keys whose silent removal during recovery is the dominant constraint_relaxation failure. When type is in the active vertical's critical set, the server returns a status=blocked_critical_filter envelope unless the user clearly asked to drop it or the caller passes confirm=True. For internal_talent only, confirm=True` is ignored when the active value is visible in the original query_context; that closes the single-turn recovery bypass that drops employee/org anchors. WARNING: Removing 'companies' is almost always wrong. If the query specifies an industry or sector, keep that filter and broaden titles instead. Returning candidates at the WRONG industry is a critical precision error.

remove_filter

ChatGPT
Remove a filter value, or clear all values for a field. To remove a specific value: remove_filter("companies", "Google") To clear entire field: remove_filter("companies") If the user's most recent message doesn't look like a drop instruction (e.g. "focus on must-haves" is a priority hint, not a drop), the server attaches an `advisory field to the response. The removal still proceeds — the advisory is informational only. Critical-filter anchor (non-public verticals only): each vertical declares a set of filter keys whose silent removal during recovery is the dominant constraint_relaxation failure. When type is in the active vertical's critical set, the server returns a status=blocked_critical_filter envelope unless the user clearly asked to drop it or the caller passes confirm=True. For internal_talent only, confirm=True` is ignored when the active value is visible in the original query_context; that closes the single-turn recovery bypass that drops employee/org anchors. WARNING: Removing 'companies' is almost always wrong. If the query specifies an industry or sector, keep that filter and broaden titles instead. Returning candidates at the WRONG industry is a critical precision error.

remove_filter

ChatGPT
Remove a filter value, or clear all values for a field. To remove a specific value: remove_filter("companies", "Google") To clear entire field: remove_filter("companies") If the user's most recent message doesn't look like a drop instruction (e.g. "focus on must-haves" is a priority hint, not a drop), the server attaches an `advisory field to the response. The removal still proceeds — the advisory is informational only. Critical-filter anchor (non-public verticals only): each vertical declares a set of filter keys whose silent removal during recovery is the dominant constraint_relaxation failure. When type is in the active vertical's critical set, the server returns a status=blocked_critical_filter envelope unless the user clearly asked to drop it or the caller passes confirm=True. For internal_talent only, confirm=True` is ignored when the active value is visible in the original query_context; that closes the single-turn recovery bypass that drops employee/org anchors. WARNING: Removing 'companies' is almost always wrong. If the query specifies an industry or sector, keep that filter and broaden titles instead. Returning candidates at the WRONG industry is a critical precision error.

resolve_skills_for_org

ChatGPT
Map free-text skill terms onto the active org's real skill vocabulary. Use BEFORE `add_filter("skills", ...) (or add_filter("internal_skills", ...)) on internal_talent sessions to confirm that the term matches what the org actually has indexed. Returns three buckets: - mapped: direct matches in the org vocabulary. - synonyms: close alternatives — the LLM can decide whether to expand or pick. - unmatched: terms with no reasonable match — surface to the recruiter rather than silently filtering for nothing. Args: target_skills: free-text skill terms to map. org_domain: org_domain override (defaults to the authenticated org via describe_org_schema`). use_org_context: when False, returns the input unchanged with no LLM call. Default True for internal_talent.

resolve_skills_for_org

ChatGPT
Map free-text skill terms onto the active org's real skill vocabulary. Use BEFORE `add_filter("skills", ...) (or add_filter("internal_skills", ...)) on internal_talent sessions to confirm that the term matches what the org actually has indexed. Returns three buckets: - mapped: direct matches in the org vocabulary. - synonyms: close alternatives — the LLM can decide whether to expand or pick. - unmatched: terms with no reasonable match — surface to the recruiter rather than silently filtering for nothing. Args: target_skills: free-text skill terms to map. org_domain: org_domain override (defaults to the authenticated org via describe_org_schema`). use_org_context: when False, returns the input unchanged with no LLM call. Default True for internal_talent.

resolve_skills_for_org

ChatGPT
Map free-text skill terms onto the active org's real skill vocabulary. Use BEFORE `add_filter("skills", ...) (or add_filter("internal_skills", ...)) on internal_talent sessions to confirm that the term matches what the org actually has indexed. Returns three buckets: - mapped: direct matches in the org vocabulary. - synonyms: close alternatives — the LLM can decide whether to expand or pick. - unmatched: terms with no reasonable match — surface to the recruiter rather than silently filtering for nothing. Args: target_skills: free-text skill terms to map. org_domain: org_domain override (defaults to the authenticated org via describe_org_schema`). use_org_context: when False, returns the input unchanged with no LLM call. Default True for internal_talent.

search_entities

ChatGPT
Look up an entity by name. Returns matching options with names and counts. ALWAYS call this BEFORE add_filter to validate entity names exist. Dispatches to the correct autosuggest endpoint based on the active vertical. type: Filter type name to look up. Use the same name as for add_filter: - Public profiles: "company" | "title" | "skill" | "location" | "school" | "major" | "degree" | "certification" - Other verticals: use filter names from available_filters (e.g., "conferences", "specialties", "topics", "nurse_type") query: Partial name to search (e.g., "neur" for NeurIPS, "card" for Cardiology) max_results: Number of suggestions to return (default 5) Returns: {suggestions: [{name, count}, ...]}

search_entities

ChatGPT
Look up an entity by name. Returns matching options with names and counts. ALWAYS call this BEFORE add_filter to validate entity names exist. Dispatches to the correct autosuggest endpoint based on the active vertical. type: Filter type name to look up. Use the same name as for add_filter: - Public profiles: "company" | "title" | "skill" | "location" | "school" | "major" | "degree" | "certification" - Other verticals: use filter names from available_filters (e.g., "conferences", "specialties", "topics", "nurse_type") query: Partial name to search (e.g., "neur" for NeurIPS, "card" for Cardiology) max_results: Number of suggestions to return (default 5) Returns: {suggestions: [{name, count}, ...]}

search_entities

ChatGPT
Look up an entity by name. Returns matching options with names and counts. ALWAYS call this BEFORE add_filter to validate entity names exist. Dispatches to the correct autosuggest endpoint based on the active vertical. type: Filter type name to look up. Use the same name as for add_filter: - Public profiles: "company" | "title" | "skill" | "location" | "school" | "major" | "degree" | "certification" - Other verticals: use filter names from available_filters (e.g., "conferences", "specialties", "topics", "nurse_type") query: Partial name to search (e.g., "neur" for NeurIPS, "card" for Cardiology) max_results: Number of suggestions to return (default 5) Returns: {suggestions: [{name, count}, ...]}

search_entities_batch

ChatGPT
Look up multiple entities at once. Much faster than calling search_entities repeatedly. Use when you need to look up many values for a filter type at once. Dispatches to the correct autosuggest endpoint based on the active vertical. type: Filter type name (same as for add_filter / search_entities) queries: List of entity names to look up max_results: Suggestions per query (default 3) Returns: {resolved: [{query, name, count}], unresolved: [...], total_resolved, total_unresolved} Example: search_entities_batch(type="conferences", queries=["NeurIPS", "ICML", "AAAI"])

search_entities_batch

ChatGPT
Look up multiple entities at once. Much faster than calling search_entities repeatedly. Use when you need to look up many values for a filter type at once. Dispatches to the correct autosuggest endpoint based on the active vertical. type: Filter type name (same as for add_filter / search_entities) queries: List of entity names to look up max_results: Suggestions per query (default 3) Returns: {resolved: [{query, name, count}], unresolved: [...], total_resolved, total_unresolved} Example: search_entities_batch(type="conferences", queries=["NeurIPS", "ICML", "AAAI"])

search_entities_batch

ChatGPT
Look up multiple entities at once. Much faster than calling search_entities repeatedly. Use when you need to look up many values for a filter type at once. Dispatches to the correct autosuggest endpoint based on the active vertical. type: Filter type name (same as for add_filter / search_entities) queries: List of entity names to look up max_results: Suggestions per query (default 3) Returns: {resolved: [{query, name, count}], unresolved: [...], total_resolved, total_unresolved} Example: search_entities_batch(type="conferences", queries=["NeurIPS", "ICML", "AAAI"])

search_people

ChatGPT
Execute a search using ALL accumulated session filters. Requires at least one filter or a boolean query in the session; a title specifically is no longer required — a skills boolean, a company or location filter, or any other constraint is accepted. Entity names are resolved at query time. Returns candidates with match_signals. Returns: {total_count, returned, candidates: [...], facets: {...}} Each candidate includes recruiter-useful match_signals such as function_mismatch, location_match, and skill_match when available. Review match_signals — exclude candidates with function_mismatch or location_match=false. skill_match shows which query skill terms (e.g. "MongoDB") appear in the candidate's full skills list. If skill_match=false, the candidate lacks the required skill — no need to fetch their full profile. If 0 results with location filters: reports global_count_without_location to guide relaxation. Broaden TITLE variants within the same function first. Relax location as a last resort.

search_people

ChatGPT
Execute a search using ALL accumulated session filters. Requires at least one filter or a boolean query in the session; a title specifically is no longer required — a skills boolean, a company or location filter, or any other constraint is accepted. Entity names are resolved at query time. Returns candidates with match_signals. Returns: {total_count, returned, candidates: [...], facets: {...}} Each candidate includes recruiter-useful match_signals such as function_mismatch, location_match, and skill_match when available. Review match_signals — exclude candidates with function_mismatch or location_match=false. skill_match shows which query skill terms (e.g. "MongoDB") appear in the candidate's full skills list. If skill_match=false, the candidate lacks the required skill — no need to fetch their full profile. If 0 results with location filters: reports global_count_without_location to guide relaxation. Broaden TITLE variants within the same function first. Relax location as a last resort.

search_people

ChatGPT
Execute a search using ALL accumulated session filters. Requires at least one filter or a boolean query in the session; a title specifically is no longer required — a skills boolean, a company or location filter, or any other constraint is accepted. Entity names are resolved at query time. Returns candidates with match_signals. Returns: {total_count, returned, candidates: [...], facets: {...}} Each candidate includes recruiter-useful match_signals such as function_mismatch, location_match, and skill_match when available. Review match_signals — exclude candidates with function_mismatch or location_match=false. skill_match shows which query skill terms (e.g. "MongoDB") appear in the candidate's full skills list. If skill_match=false, the candidate lacks the required skill — no need to fetch their full profile. If 0 results with location filters: reports global_count_without_location to guide relaxation. Broaden TITLE variants within the same function first. Relax location as a last resort.

set_index

ChatGPT
Set the geographic index for searches. Default is NorthAmerica. Available indexes: NorthAmerica, Europe, Asia, Africa, Oceania, SouthAmerica, all Index mapping: NorthAmerica: US, Canada, Mexico Europe: UK, Germany, France, Sweden, Netherlands, Denmark, Spain, Ireland, Italy, Finland, ... Asia: India, China, Japan, Singapore, UAE, Saudi Arabia, Israel, Philippines, ... SouthAmerica: Brazil, Argentina, Colombia, Chile, ... Oceania: Australia, New Zealand Africa: South Africa, Nigeria, Kenya, ... all: fan out across all geographic indexes

set_index

ChatGPT
Set the geographic index for searches. Default is NorthAmerica. Available indexes: NorthAmerica, Europe, Asia, Africa, Oceania, SouthAmerica, all Index mapping: NorthAmerica: US, Canada, Mexico Europe: UK, Germany, France, Sweden, Netherlands, Denmark, Spain, Ireland, Italy, Finland, ... Asia: India, China, Japan, Singapore, UAE, Saudi Arabia, Israel, Philippines, ... SouthAmerica: Brazil, Argentina, Colombia, Chile, ... Oceania: Australia, New Zealand Africa: South Africa, Nigeria, Kenya, ... all: fan out across all geographic indexes

set_index

ChatGPT
Set the geographic index for searches. Default is NorthAmerica. Available indexes: NorthAmerica, Europe, Asia, Africa, Oceania, SouthAmerica, all Index mapping: NorthAmerica: US, Canada, Mexico Europe: UK, Germany, France, Sweden, Netherlands, Denmark, Spain, Ireland, Italy, Finland, ... Asia: India, China, Japan, Singapore, UAE, Saudi Arabia, Israel, Philippines, ... SouthAmerica: Brazil, Argentina, Colombia, Chile, ... Oceania: Australia, New Zealand Africa: South Africa, Nigeria, Kenya, ... all: fan out across all geographic indexes

set_query

ChatGPT
Set or replace the boolean search expression. Typically contains `cur_title:() for role matching, but that is not required — skills-only, seniority-only, or company-name-only booleans are accepted. ALWAYS include user_intent (user's exact message) and user_intent_category`. boolean_query: The boolean search string. Recommended pattern: cur_title:("Policy Manager") for the role plus free-text skill keywords: cur_title:("Engineer") AND ("Python" OR "React"). Always quote multi-word titles. NEVER use skills:() syntax — it misses up to 49% of candidates. Use AND between must-have skill families and OR only inside a family for synonyms or acceptable alternatives. Do not use true:y or caret boosts for preferred terms. query_context: Original query text (e.g. "etl statisticians in LA"). Keep this stable across iterative refinements when possible. Used by add_filter("titles", ...) to detect function mismatches and keyword drops. If you start a genuinely new search in the same session, send the new query_context and stale filters will be cleared automatically. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

set_query

ChatGPT
Set or replace the boolean search expression. Typically contains `cur_title:() for role matching, but that is not required — skills-only, seniority-only, or company-name-only booleans are accepted. ALWAYS include user_intent (user's exact message) and user_intent_category`. boolean_query: The boolean search string. Recommended pattern: cur_title:("Policy Manager") for the role plus free-text skill keywords: cur_title:("Engineer") AND ("Python" OR "React"). Always quote multi-word titles. NEVER use skills:() syntax — it misses up to 49% of candidates. Use AND between must-have skill families and OR only inside a family for synonyms or acceptable alternatives. Do not use true:y or caret boosts for preferred terms. query_context: Original query text (e.g. "etl statisticians in LA"). Keep this stable across iterative refinements when possible. Used by add_filter("titles", ...) to detect function mismatches and keyword drops. If you start a genuinely new search in the same session, send the new query_context and stale filters will be cleared automatically. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

set_query

ChatGPT
Set or replace the boolean search expression. Typically contains `cur_title:() for role matching, but that is not required — skills-only, seniority-only, or company-name-only booleans are accepted. ALWAYS include user_intent (user's exact message) and user_intent_category`. boolean_query: The boolean search string. Recommended pattern: cur_title:("Policy Manager") for the role plus free-text skill keywords: cur_title:("Engineer") AND ("Python" OR "React"). Always quote multi-word titles. NEVER use skills:() syntax — it misses up to 49% of candidates. Use AND between must-have skill families and OR only inside a family for synonyms or acceptable alternatives. Do not use true:y or caret boosts for preferred terms. query_context: Original query text (e.g. "etl statisticians in LA"). Keep this stable across iterative refinements when possible. Used by add_filter("titles", ...) to detect function mismatches and keyword drops. If you start a genuinely new search in the same session, send the new query_context and stale filters will be cleared automatically. user_intent: The user's latest raw unedited message. ALWAYS pass the user's original request exactly as they stated it. user_intent_category: One of: hire, research, competitive_intel, market_sizing, outreach, other.

set_vertical

ChatGPT
Switch search to a different index vertical. Available verticals: public, github, academic, healthcare, nursing, ats, internal_talent Default is 'public' (standard talent search). Only switch when the user explicitly asks to search a specific index. For ATS with multiple connections: pass connection_id to select one, or omit to see available connections. Switching verticals resets filters (different verticals have different filter types). The response includes available_filters for the new vertical.

set_vertical

ChatGPT
Switch search to a different index vertical. Available verticals: public, github, academic, healthcare, nursing, ats, internal_talent Default is 'public' (standard talent search). Only switch when the user explicitly asks to search a specific index. For ATS with multiple connections: pass connection_id to select one, or omit to see available connections. Switching verticals resets filters (different verticals have different filter types). The response includes available_filters for the new vertical.

set_vertical

ChatGPT
Switch search to a different index vertical. Available verticals: public, github, academic, healthcare, nursing, ats, internal_talent Default is 'public' (standard talent search). Only switch when the user explicitly asks to search a specific index. For ATS with multiple connections: pass connection_id to select one, or omit to see available connections. Switching verticals resets filters (different verticals have different filter types). The response includes available_filters for the new vertical.

Capabilities

WritesInteractive

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App Stats

114

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1

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ChatGPT

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Job Search

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ChatGPT

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