list_available_tools
ChatGPTShow all CreditAI search tools and which ones your account can access. Returns: - policy: batch-call policy header — read this first; it states why you must pair general-tier and specialized tools in the same batch. - tools: every tool with has_access indicating whether your account is licensed for it. Each entry returns the same description the tool advertises in its MCP registration plus a short title and tier (general or specialized). - notice (only when present): the account is not enabled for the Octus MCP — every tool reports has_access: false and search calls will be denied. Relay this message to the user instead of retrying. When the user's question is vague or could plausibly fit multiple sources, call this tool and present the accessible tools to the user as a multiple-choice picker (your client's own user-input UI) so they can pick one or more sources before you run a search. Use a per-source tool when a single source is picked, when the user picks several or asks for breadth, run the picked tools in sequential waves of two (the 2-in-flight concurrency cap). Do not call this on every turn — only when disambiguation is genuinely needed.
list_available_tools
ChatGPTShow all CreditAI search tools and which ones your account can access. Returns: - policy: batch-call policy header — read this first; it states why you must pair general-tier and specialized tools in the same batch. - tools: every tool with has_access indicating whether your account is licensed for it. Each entry returns the same description the tool advertises in its MCP registration plus a short title and tier (general or specialized). - notice (only when present): the account is not enabled for the Octus MCP — every tool reports has_access: false and search calls will be denied. Relay this message to the user instead of retrying. When the user's question is vague or could plausibly fit multiple sources, call this tool and present the accessible tools to the user as a multiple-choice picker (your client's own user-input UI) so they can pick one or more sources before you run a search. Use a per-source tool when a single source is picked, when the user picks several or asks for breadth, run the picked tools in sequential waves of two (the 2-in-flight concurrency cap). Do not call this on every turn — only when disambiguation is genuinely needed.
search_court_filings
ChatGPTSearch Court Filings on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). AI-summarized court filings from US district courts. Good for case-level questions and litigation context. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_court_filings
ChatGPTSearch Court Filings on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). AI-summarized court filings from US district courts. Good for case-level questions and litigation context. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_covenants_private
ChatGPTSearch Covenants Private Docs and Reports on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Covenant analysis over private-credit debt documents accessible to the user (sourced from their FinDox data rooms and Deal Term Analytics coverage). Good for the same covenant questions as the public tool — clause comparison, precedent retrieval, provision history, liability-management risk — applied to private-credit deals. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_covenants_private
ChatGPTSearch Covenants Private Docs and Reports on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Covenant analysis over private-credit debt documents accessible to the user (sourced from their FinDox data rooms and Deal Term Analytics coverage). Good for the same covenant questions as the public tool — clause comparison, precedent retrieval, provision history, liability-management risk — applied to private-credit deals. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_covenants_public
ChatGPTSearch Covenants Public Docs and Reports on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Covenant analysis over public-side debt documents — credit agreements, indentures, offering memorandums, and tear sheets for syndicated loans and high-yield bonds. Good for covenant questions: comparing covenant packages, retrieving precedent clauses, understanding provision meaning and history, and spotting liability-management exposure (uptiering, drop-downs, etc.) on public credits. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_covenants_public
ChatGPTSearch Covenants Public Docs and Reports on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Covenant analysis over public-side debt documents — credit agreements, indentures, offering memorandums, and tear sheets for syndicated loans and high-yield bonds. Good for covenant questions: comparing covenant packages, retrieving precedent clauses, understanding provision meaning and history, and spotting liability-management exposure (uptiering, drop-downs, etc.) on public credits. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_data_rooms
ChatGPTSearch Data Room Docs on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). FinDox data rooms. Holds a wide range of materials per deal (analysis, earnings, transcripts, memos, legal docs, etc.). Good for any question grounded in the user's accessible deal data rooms. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_data_rooms
ChatGPTSearch Data Room Docs on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). FinDox data rooms. Holds a wide range of materials per deal (analysis, earnings, transcripts, memos, legal docs, etc.). Good for any question grounded in the user's accessible deal data rooms. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_deal_term_analytics
ChatGPTSearch Deal Term Analytics on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Deal Term Analytics — granular deal-term data extracted from credit documents and benchmarked against Octus's global library, with 170+ data fields per instrument across syndicated loans and private credit. Good for market-wide deal-term comparisons, identifying market-standard vs. outlier terms, screening by industry / sponsor / facility size, and portfolio-level term tracking. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_deal_term_analytics
ChatGPTSearch Deal Term Analytics on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Deal Term Analytics — granular deal-term data extracted from credit documents and benchmarked against Octus's global library, with 170+ data fields per instrument across syndicated loans and private credit. Good for market-wide deal-term comparisons, identifying market-standard vs. outlier terms, screening by industry / sponsor / facility size, and portfolio-level term tracking. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_fundamentals
ChatGPTSearch Fundamentals Data on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Fundamentals data on companies — both structured and unstructured. Structured side: revenue, EBITDA, leverage, interest coverage, margins, multiples, cash flow, and other reported financials as quarterly / annual time series. Unstructured side: company-disclosed documents including earnings presentations and reports, offerings / lender memoranda, capital-structure docs, press releases, sustainability reports, transcripts and transcript summaries, and compliance certificates. Good for quantitative questions, benchmarking, trend lookups, or anything grounded in these company documents. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_fundamentals
ChatGPTSearch Fundamentals Data on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Fundamentals data on companies — both structured and unstructured. Structured side: revenue, EBITDA, leverage, interest coverage, margins, multiples, cash flow, and other reported financials as quarterly / annual time series. Unstructured side: company-disclosed documents including earnings presentations and reports, offerings / lender memoranda, capital-structure docs, press releases, sustainability reports, transcripts and transcript summaries, and compliance certificates. Good for quantitative questions, benchmarking, trend lookups, or anything grounded in these company documents. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_intel
ChatGPTSearch Octus Intel on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). Analyst-written articles, notes, and reports across Octus's editorial coverage: Covenants Analysis — in-depth analysis on US/EU bonds and loans; Credit Intelligence — insights across the entire sub-investment-grade universe; First Day Intelligence — analysis when new Chapter 11 cases are filed; Middle Market Intelligence — actionable intelligence on middle-market trends and transactions; Municipals Intelligence — insights on high-yield municipal issuers and credit trends; Private Company Analysis — in-depth research into private companies across the credit lifecycle; Private Credit & Deal Origination — private-credit and origination intelligence; Structured Finance — CLO market news, manager performance, and portfolio analytics. Use this for the analyst narrative or commentary on any of these areas. Use a source-specific tool instead when the user wants the underlying document. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_intel
ChatGPTSearch Octus Intel on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). Analyst-written articles, notes, and reports across Octus's editorial coverage: Covenants Analysis — in-depth analysis on US/EU bonds and loans; Credit Intelligence — single company and sector insights across the entire sub-investment-grade universe; First Day Intelligence — analysis when new Chapter 11 cases are filed; Middle Market Intelligence — actionable intelligence on middle-market trends and transactions; Municipals Intelligence — insights on high-yield municipal issuers and credit trends; Private Credit & Deal Origination — private-credit and origination intelligence; Structured Finance — CLO market news, manager performance, and portfolio analytics. Use this for the analyst narrative or commentary on any of these areas. Use a source-specific tool instead when the user wants the underlying document. If a long-form analysis has been prepared, make sure to consider this curated, expert content in your response. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_my_uploads
ChatGPTSearch My Uploads on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Search across documents the user has uploaded to CreditAI. Good for any question grounded in their own uploaded files. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_my_uploads
ChatGPTSearch My Uploads on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Search across documents the user has uploaded to CreditAI. Good for any question grounded in their own uploaded files. My Uploads only searches documents uploaded through the CreditAI web app. Files uploaded elsewhere or via other Octus products are not included. Upload documents at https://app.octus.com/v3#/creditai/myuploads. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_private_company_analysis
ChatGPTSearch Private Company Analysis on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Deep analysis on private companies, with coverage spanning broadly syndicated loans and high-yield bonds, developed by Octus's experts. Good for in-depth questions on a specific private name. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_private_company_analysis
ChatGPTSearch Private Company Analysis on CreditAI. Tier: SPECIALIZED — narrower scope, may error or return empty. Must be batched with ≥1 GENERAL-tier tool as the fallback (see server instructions). Deep analysis on private companies, with coverage spanning broadly syndicated loans and high-yield bonds, developed by Octus's experts. Good for in-depth questions on a specific private name. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_sec_filings
ChatGPTSearch SEC Filings on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). SEC filings — 8-K, 10-K, 10-Q, and DEF 14A. Good for questions grounded in what companies have disclosed. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_sec_filings
ChatGPTSearch SEC Filings on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). SEC filings — 8-K, 10-K, 10-Q, and DEF 14A. Good for questions grounded in what companies have disclosed. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_transcripts
ChatGPTSearch Transcripts on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). Company transcripts including earnings calls and other transcribed management events. Good for what management actually said, direct quotes, and call-level detail. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
search_transcripts
ChatGPTSearch Transcripts on CreditAI. Tier: GENERAL — resilience layer. Include at least one general-tier tool in every parallel batch; it's the fallback when specialized sources error or return empty (see server instructions). Company transcripts including earnings calls and other transcribed management events. Good for what management actually said, direct quotes, and call-level detail. If the user's question is vague or could plausibly fit multiple sources, call list_available_tools first and present the accessible tools to the user as a multiple-choice picker so they can pick one or more — do not guess. Always surface sources for every answer, regardless of format (list, narrative, comparison, memo) — never drop citations. Rendering: answer is a markdown string with inline [N] markers (1-indexed) and a trailing **Sources** block listing [N] [title](url) sourceSubType for each cited source. When presenting the answer to the user, build an N→(title, url) mapping from the **Sources** block, then replace each [N] marker in the prose with [title](url) (a markdown hyperlink using the title as link text). Omit the **Sources** block from the final display — the inline hyperlinks carry the same information. If a footnote line has no markdown link (URL resolution failed), render just the title text in place of [N].
