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SCOUT for Federal Contracting

by PrimeRFP

Overview

SCOUT for Federal Contracting helps govcon teams search active federal and SLED solicitations, size markets from USASpending awards, track recompete pipelines, review GAO protests, and run executive agency briefings inside ChatGPT.

Tools

analyze_capture_opportunity

ChatGPT
Use this when the user wants a full capture-ready intelligence package for a specific opportunity space — useful for bid/no-bid decisions, capture planning, and win-strategy formulation. Returns a computed GO / EXPLORE / NO-GO recommendation. Aggregates five factors in parallel: active solicitations matching the space, budget backing, competitive landscape (who's winning and incumbent strength), agency protest risk, and recompete timing. The recommendation scores those factors: GO (4-5 positive), EXPLORE (2-3 positive), NO-GO (0-1 positive). For agency-wide overviews (not program-specific), use get_agency_briefing.

analyze_capture_opportunity

ChatGPT
Use this when the user wants a full capture-ready intelligence package for a specific opportunity space — useful for bid/no-bid decisions, capture planning, and win-strategy formulation. Returns a computed GO / EXPLORE / NO-GO recommendation. Aggregates five factors in parallel: active solicitations matching the space, budget backing, competitive landscape (who's winning and incumbent strength), agency protest risk, and recompete timing. The recommendation scores those factors: GO (4-5 positive), EXPLORE (2-3 positive), NO-GO (0-1 positive). For agency-wide overviews (not program-specific), use get_agency_briefing.

analyze_capture_opportunity

ChatGPT
Use this when the user wants a full capture-ready intelligence package for a specific opportunity space — useful for bid/no-bid decisions, capture planning, and win-strategy formulation. Returns a computed GO / EXPLORE / NO-GO recommendation. Aggregates five factors in parallel: active solicitations matching the space, budget backing, competitive landscape (who's winning and incumbent strength), agency protest risk, and recompete timing. The recommendation scores those factors: GO (4-5 positive), EXPLORE (2-3 positive), NO-GO (0-1 positive). For agency-wide overviews (not program-specific), use get_agency_briefing.

analyze_protest_patterns

ChatGPT
Use this when the user wants cross-case pattern analysis over GAO bid-protest decisions — agency sustain rates, outcome predictors by issue tag, FAR-citation effectiveness, repeat-protester patterns, incumbent-advantage signals, or tag co-occurrence clusters. Filter with agency (use "DoD" to match Navy, Army, Air Force, Marine Corps), protester, awardee, naics_code, or date range. Use focus= to refine to an issue area — examples: "technical_evaluation", "price_evaluation", "organizational_conflict_of_interest", "past_performance", "sole_source", "best_value", "disparate_treatment". For individual case detail use list_protest_decisions; for simple counts and sustain rates use get_protest_sustain_rates.

analyze_protest_patterns

ChatGPT
Use this when the user wants cross-case pattern analysis over GAO bid-protest decisions — agency sustain rates, outcome predictors by issue tag, FAR-citation effectiveness, repeat-protester patterns, incumbent-advantage signals, or tag co-occurrence clusters. Filter with agency (use "DoD" to match Navy, Army, Air Force, Marine Corps), protester, awardee, naics_code, or date range. Use focus= to refine to an issue area — examples: "technical_evaluation", "price_evaluation", "organizational_conflict_of_interest", "past_performance", "sole_source", "best_value", "disparate_treatment". For individual case detail use list_protest_decisions; for simple counts and sustain rates use get_protest_sustain_rates.

analyze_protest_patterns

ChatGPT
Use this when the user wants cross-case pattern analysis over GAO bid-protest decisions — agency sustain rates, outcome predictors by issue tag, FAR-citation effectiveness, repeat-protester patterns, incumbent-advantage signals, or tag co-occurrence clusters. Filter with agency (use "DoD" to match Navy, Army, Air Force, Marine Corps), protester, awardee, naics_code, or date range. Use focus= to refine to an issue area — examples: "technical_evaluation", "price_evaluation", "organizational_conflict_of_interest", "past_performance", "sole_source", "best_value", "disparate_treatment". For individual case detail use list_protest_decisions; for simple counts and sustain rates use get_protest_sustain_rates.

ebuy_notifications

ChatGPT
Use this when the user wants to view parsed GSA eBuy RFQ notifications — filtered by importance, type, status, NAICS, agency, or contract vehicle. Each notification includes RFQ details, importance scoring, triage summary, and recommended action items. Filter by importance ("high", "medium", "low"), notification_type ("new_rfq", "amendment", "cancellation", "award_notice", "extension"), or status ("unread", "read", "actioned", "dismissed").

ebuy_notifications

ChatGPT
Use this when the user wants to view parsed GSA eBuy RFQ notifications — filtered by importance, type, status, NAICS, agency, or contract vehicle. Each notification includes RFQ details, importance scoring, triage summary, and recommended action items. Filter by importance ("high", "medium", "low"), notification_type ("new_rfq", "amendment", "cancellation", "award_notice", "extension"), or status ("unread", "read", "actioned", "dismissed").

ebuy_notifications

ChatGPT
Use this when the user wants to view parsed GSA eBuy RFQ notifications — filtered by importance, type, status, NAICS, agency, or contract vehicle. Each notification includes RFQ details, importance scoring, triage summary, and recommended action items. Filter by importance ("high", "medium", "low"), notification_type ("new_rfq", "amendment", "cancellation", "award_notice", "extension"), or status ("unread", "read", "actioned", "dismissed").

find_pipeline_forecasts

ChatGPT
Use when the user asks about pre-solicitation acquisition forecasts, agency planning dashboards, or opportunities before they appear on SAM.gov (DHS APFS, DOT, DOJ, GSA FCO). Returns planning data only — not live solicitations. For awarded/recompete data use find_recompete_contracts.

find_pipeline_forecasts

ChatGPT
Use when the user asks about pre-solicitation acquisition forecasts, agency planning dashboards, or opportunities before they appear on SAM.gov (DHS APFS, DOT, DOJ, GSA FCO). Returns planning data only — not live solicitations. For awarded/recompete data use find_recompete_contracts.

find_pipeline_forecasts

ChatGPT
Use when the user asks about pre-solicitation acquisition forecasts, agency planning dashboards, or opportunities before they appear on SAM.gov (DHS APFS, DOT, DOJ, GSA FCO). Returns planning data only — not live solicitations. For awarded/recompete data use find_recompete_contracts.

find_policy_intel

ChatGPT
Use when the user asks about congressional hearing signals — NDAA, appropriations, budget testimony, procurement policy, or committee oversight affecting federal contracting. Returns structured intel chunks from transcribed hearings (not live SAM data). Pair with find_pipeline_forecasts or find_recompete_contracts for full capture context.

find_policy_intel

ChatGPT
Use when the user asks about congressional hearing signals — NDAA, appropriations, budget testimony, procurement policy, or committee oversight affecting federal contracting. Returns structured intel chunks from transcribed hearings (not live SAM data). Pair with find_pipeline_forecasts or find_recompete_contracts for full capture context.

find_policy_intel

ChatGPT
Use when the user asks about congressional hearing signals — NDAA, appropriations, budget testimony, procurement policy, or committee oversight affecting federal contracting. Returns structured intel chunks from transcribed hearings (not live SAM data). Pair with find_pipeline_forecasts or find_recompete_contracts for full capture context.

find_recompete_contracts

ChatGPT
Use this when the user asks about contracts coming up for re-bid — incumbent research, pipeline building, capture planning, or expiring-contract discovery. Returns incumbent, estimated value, period-of-performance end date, and days until expiry. Set within_months to the horizon the user asked for (default 24, max 60): "next 6 months" -> 6, "next 2 years" -> 24, "next 5 years" -> 60. Set include_historical=true to include awards whose PoP already ended within the same window (+/- within_months). For value-targeted questions ("over $100M", "between $10M and $50M"), set min_value / max_value in raw dollars (e.g. 100000000 for $100M) — the filter is applied server-side before the detail-row cap so big-ticket contracts aren't dropped. Set sort_by="value" to rank returned rows by estimated value descending, "expiry" to rank by soonest PoP end, or omit for the default relevance score. The response always includes a pipeline_by_value_band histogram (counts and totals in <$1M / $1M–$10M / $10M–$100M / ≥$100M buckets) over the full matched pipeline, so you can answer "how many ≥ $100M" without paging through detail rows. DATE FIELDS (per row): - pop_end_current / days_until_pop_end_current — soonest date a recompete must be in place (current PoP end, before any options are exercised). Use this for capture-planning urgency. - pop_end_potential / expiry_date (= same value) and days_until_expiry — latest the work could continue if every remaining option is exercised. expiry_date is preserved for backward compatibility with widgets; new consumers should prefer pop_end_potential to make the option-period semantic explicit. A contract whose options have not yet been exercised will show pop_end_current years earlier than pop_end_potential — both are real signals. For award history and spending totals, use get_award_history.

find_recompete_contracts

ChatGPT
Use this when the user asks about contracts coming up for re-bid — incumbent research, pipeline building, capture planning, or expiring-contract discovery. Returns incumbent, estimated value, period-of-performance end date, and days until expiry. Set within_months to the horizon the user asked for (default 24, max 60): "next 6 months" -> 6, "next 2 years" -> 24, "next 5 years" -> 60. Set include_historical=true to include awards whose PoP already ended within the same window (+/- within_months). For value-targeted questions ("over $100M", "between $10M and $50M"), set min_value / max_value in raw dollars (e.g. 100000000 for $100M) — the filter is applied server-side before the detail-row cap so big-ticket contracts aren't dropped. Set sort_by="value" to rank returned rows by estimated value descending, "expiry" to rank by soonest PoP end, or omit for the default relevance score. The response always includes a pipeline_by_value_band histogram (counts and totals in <$1M / $1M–$10M / $10M–$100M / ≥$100M buckets) over the full matched pipeline, so you can answer "how many ≥ $100M" without paging through detail rows. DATE FIELDS (per row): - pop_end_current / days_until_pop_end_current — soonest date a recompete must be in place (current PoP end, before any options are exercised). Use this for capture-planning urgency. - pop_end_potential / expiry_date (= same value) and days_until_expiry — latest the work could continue if every remaining option is exercised. expiry_date is preserved for backward compatibility with widgets; new consumers should prefer pop_end_potential to make the option-period semantic explicit. A contract whose options have not yet been exercised will show pop_end_current years earlier than pop_end_potential — both are real signals. For award history and spending totals, use get_award_history.

find_recompete_contracts

ChatGPT
Use this when the user asks about contracts coming up for re-bid — incumbent research, pipeline building, capture planning, or expiring-contract discovery. Returns incumbent, estimated value, period-of-performance end date, and days until expiry. Set within_months to the horizon the user asked for (default 24, max 60): "next 6 months" -> 6, "next 2 years" -> 24, "next 5 years" -> 60. Set include_historical=true to include awards whose PoP already ended within the same window (+/- within_months). For value-targeted questions ("over $100M", "between $10M and $50M"), set min_value / max_value in raw dollars (e.g. 100000000 for $100M) — the filter is applied server-side before the detail-row cap so big-ticket contracts aren't dropped. Set sort_by="value" to rank returned rows by estimated value descending, "expiry" to rank by soonest PoP end, or omit for the default relevance score. The response always includes a pipeline_by_value_band histogram (counts and totals in <$1M / $1M–$10M / $10M–$100M / ≥$100M buckets) over the full matched pipeline, so you can answer "how many ≥ $100M" without paging through detail rows. DATE FIELDS (per row): - pop_end_current / days_until_pop_end_current — soonest date a recompete must be in place (current PoP end, before any options are exercised). Use this for capture-planning urgency. - pop_end_potential / expiry_date (= same value) and days_until_expiry — latest the work could continue if every remaining option is exercised. expiry_date is preserved for backward compatibility with widgets; new consumers should prefer pop_end_potential to make the option-period semantic explicit. A contract whose options have not yet been exercised will show pop_end_current years earlier than pop_end_potential — both are real signals. For award history and spending totals, use get_award_history.

find_subawards

ChatGPT
Use this when the user wants disclosed federal subawards — by prime PIID, subrecipient name or UEI, or as an aggregate across a class of prime contracts. Modes (highest-precedence argument wins): piid -> subawards under a specific prime; subrecipient_uei -> exact UEI match; subrecipient_name -> substring on subrecipient; prime_awardee_name -> who a named prime typically subcontracts to; prime_naics_prefix + prime_keyword -> aggregate ranked subrecipients across a class of primes (example: prime_naics_prefix="5415" with prime_keyword="cybersecurity"). Pair with get_award_history when the user starts with a prime contractor and wants the full picture.

find_subawards

ChatGPT
Use this when the user wants disclosed federal subawards — by prime PIID, subrecipient name or UEI, or as an aggregate across a class of prime contracts. Modes (highest-precedence argument wins): piid -> subawards under a specific prime; subrecipient_uei -> exact UEI match; subrecipient_name -> substring on subrecipient; prime_awardee_name -> who a named prime typically subcontracts to; prime_naics_prefix + prime_keyword -> aggregate ranked subrecipients across a class of primes (example: prime_naics_prefix="5415" with prime_keyword="cybersecurity"). Pair with get_award_history when the user starts with a prime contractor and wants the full picture.

find_subawards

ChatGPT
Use this when the user wants disclosed federal subawards — by prime PIID, subrecipient name or UEI, or as an aggregate across a class of prime contracts. Modes (highest-precedence argument wins): piid -> subawards under a specific prime; subrecipient_uei -> exact UEI match; subrecipient_name -> substring on subrecipient; prime_awardee_name -> who a named prime typically subcontracts to; prime_naics_prefix + prime_keyword -> aggregate ranked subrecipients across a class of primes (example: prime_naics_prefix="5415" with prime_keyword="cybersecurity"). Pair with get_award_history when the user starts with a prime contractor and wants the full picture.

find_teaming_partners

ChatGPT
Use this when the user wants teaming partners whose capabilities complement theirs on a bid — including 8(a), SDVOSB, WOSB, or HUBZone partners. Matches public teaming profiles on NAICS, capabilities, and certifications. opportunity_uid is optional. When supplied (from search_opportunities), the call is enriched with NAICS and title keywords from that solicitation; otherwise pass capabilities= (required keywords) with optional naics_code and certifications. For past prime-contract winners (not a teaming recommendation), use get_award_history. For disclosed subcontracts under a specific PIID, use find_subawards.

find_teaming_partners

ChatGPT
Use this when the user wants teaming partners whose capabilities complement theirs on a bid — including 8(a), SDVOSB, WOSB, or HUBZone partners. Matches public teaming profiles on NAICS, capabilities, and certifications. opportunity_uid is optional. When supplied (from search_opportunities), the call is enriched with NAICS and title keywords from that solicitation; otherwise pass capabilities= (required keywords) with optional naics_code and certifications. For past prime-contract winners (not a teaming recommendation), use get_award_history. For disclosed subcontracts under a specific PIID, use find_subawards.

find_teaming_partners

ChatGPT
Use this when the user wants teaming partners whose capabilities complement theirs on a bid — including 8(a), SDVOSB, WOSB, or HUBZone partners. Matches public teaming profiles on NAICS, capabilities, and certifications. opportunity_uid is optional. When supplied (from search_opportunities), the call is enriched with NAICS and title keywords from that solicitation; otherwise pass capabilities= (required keywords) with optional naics_code and certifications. For past prime-contract winners (not a teaming recommendation), use get_award_history. For disclosed subcontracts under a specific PIID, use find_subawards.

generate_discovery_report

ChatGPT
Use this when the user wants a curated, AI-ranked opportunity discovery report across all active solicitations — federal, state/local, education, and commercial — filtered by keywords, NAICS, and/or agencies. report_type accepts "quick_insights", "detailed_analysis", or "trending_opportunities". Pass fresh=true to bypass any cached report. Reports typically complete in 1-3 minutes.

generate_discovery_report

ChatGPT
Use this when the user wants a curated, AI-ranked opportunity discovery report across all active solicitations — federal, state/local, education, and commercial — filtered by keywords, NAICS, and/or agencies. report_type accepts "quick_insights", "detailed_analysis", or "trending_opportunities". Pass fresh=true to bypass any cached report. Reports typically complete in 1-3 minutes.

generate_discovery_report

ChatGPT
Use this when the user wants a curated, AI-ranked opportunity discovery report across all active solicitations — federal, state/local, education, and commercial — filtered by keywords, NAICS, and/or agencies. report_type accepts "quick_insights", "detailed_analysis", or "trending_opportunities". Pass fresh=true to bypass any cached report. Reports typically complete in 1-3 minutes.

get_account_info

ChatGPT
Use this when the user wants to see their current account profile — plan, MCP tool access, monthly call limit, current usage, and account status.

get_account_info

ChatGPT
Use this when the user wants to see their current account profile — plan, MCP tool access, monthly call limit, current usage, and account status.

get_account_info

ChatGPT
Use this when the user wants to see their current account profile — plan, MCP tool access, monthly call limit, current usage, and account status.

get_agency_briefing

ChatGPT
Use this when the user wants an executive intelligence briefing on a federal agency before a meeting, gate review, or capture strategy session. Aggregates five data sources in parallel: budget priorities from Congressional Budget Justification, agency-wide budget trends, contracting landscape (top contractors and spend), GAO protest risk, and recompete pipeline. For program-specific capture intelligence with a GO/NO-GO recommendation, use analyze_capture_opportunity instead.

get_agency_briefing

ChatGPT
Use this when the user wants an executive intelligence briefing on a federal agency before a meeting, gate review, or capture strategy session. Aggregates five data sources in parallel: budget priorities from Congressional Budget Justification, agency-wide budget trends, contracting landscape (top contractors and spend), GAO protest risk, and recompete pipeline. For program-specific capture intelligence with a GO/NO-GO recommendation, use analyze_capture_opportunity instead.

get_agency_briefing

ChatGPT
Use this when the user wants an executive intelligence briefing on a federal agency before a meeting, gate review, or capture strategy session. Aggregates five data sources in parallel: budget priorities from Congressional Budget Justification, agency-wide budget trends, contracting landscape (top contractors and spend), GAO protest risk, and recompete pipeline. For program-specific capture intelligence with a GO/NO-GO recommendation, use analyze_capture_opportunity instead.

get_award_history

ChatGPT
Use this when the user asks what contracts a specific company has won, how much a company has been awarded (obligated or ceiling), past contract history, or agency-level historical spending. Data source: federal contract awards from USASpending, contracts over $100K. Set awardee= to a company or contractor name (partial match is fine). If awardee= receives a 12-character SAM.gov UEI it is matched as an exact UEI. Optional filters: recipient_uei, agency, naics_code, psc_code, set_aside_type. DATE FIELDS (per row): - award_date (= latest_action_date) — date of the most recent federal action against this PIID (base award OR latest modification). Sourced from action_date with MAX aggregation. - original_award_date — earliest action_date for this PIID within the loaded transaction window (typically the last 5–7 years). For contracts whose base award predates the load window, this is the earliest visible action, not the true base-award date. When precision matters for an old contract, prefer pop_start_date as the signal of when work actually began. - transaction_count — number of FPDS transactions visible for this PIID. A higher count signals an actively-modified long-running contract. - pop_start_date — period-of-performance start (when the contract's work was scheduled to begin). Each transaction carries the contract's POP start, so this stays accurate even when older base-award transactions have aged out of the load window. - data_refresh_date — USASpending warehouse row-update timestamp. NOT a contractual date; reflects only when the data feed was last refreshed. - is_undefinitized (when present) — True if the latest transaction is a UCA / letter contract that has not yet been definitized. By default, IDV / IDIQ base vehicle rows (which carry zero obligation) are excluded; each row also carries is_idv_base so the consumer can distinguish vehicles from task orders. For live open solicitations, use search_opportunities. For aggregated market statistics, use get_award_summary. For teaming-partner recommendations on a specific bid, use find_teaming_partners.

get_award_history

ChatGPT
Use this when the user asks what contracts a specific company has won, how much a company has been awarded (obligated or ceiling), past contract history, or agency-level historical spending. Data source: federal contract awards from USASpending, contracts over $100K. Set awardee= to a company or contractor name (partial match is fine). If awardee= receives a 12-character SAM.gov UEI it is matched as an exact UEI. Optional filters: recipient_uei, agency, naics_code, psc_code, set_aside_type. DATE FIELDS (per row): - award_date (= latest_action_date) — date of the most recent federal action against this PIID (base award OR latest modification). Sourced from action_date with MAX aggregation. - original_award_date — earliest action_date for this PIID within the loaded transaction window (typically the last 5–7 years). For contracts whose base award predates the load window, this is the earliest visible action, not the true base-award date. When precision matters for an old contract, prefer pop_start_date as the signal of when work actually began. - transaction_count — number of FPDS transactions visible for this PIID. A higher count signals an actively-modified long-running contract. - pop_start_date — period-of-performance start (when the contract's work was scheduled to begin). Each transaction carries the contract's POP start, so this stays accurate even when older base-award transactions have aged out of the load window. - data_refresh_date — USASpending warehouse row-update timestamp. NOT a contractual date; reflects only when the data feed was last refreshed. - is_undefinitized (when present) — True if the latest transaction is a UCA / letter contract that has not yet been definitized. By default, IDV / IDIQ base vehicle rows (which carry zero obligation) are excluded; each row also carries is_idv_base so the consumer can distinguish vehicles from task orders. For live open solicitations, use search_opportunities. For aggregated market statistics, use get_award_summary. For teaming-partner recommendations on a specific bid, use find_teaming_partners.

get_award_history

ChatGPT
Use this when the user asks what contracts a specific company has won, how much a company has been awarded (obligated or ceiling), past contract history, or agency-level historical spending. Data source: federal contract awards from USASpending, contracts over $100K. Set awardee= to a company or contractor name (partial match is fine). If awardee= receives a 12-character SAM.gov UEI it is matched as an exact UEI. Optional filters: recipient_uei, agency, naics_code, psc_code, set_aside_type. DATE FIELDS (per row): - award_date (= latest_action_date) — date of the most recent federal action against this PIID (base award OR latest modification). Sourced from action_date with MAX aggregation. - original_award_date — earliest action_date for this PIID within the loaded transaction window (typically the last 5–7 years). For contracts whose base award predates the load window, this is the earliest visible action, not the true base-award date. When precision matters for an old contract, prefer pop_start_date as the signal of when work actually began. - transaction_count — number of FPDS transactions visible for this PIID. A higher count signals an actively-modified long-running contract. - pop_start_date — period-of-performance start (when the contract's work was scheduled to begin). Each transaction carries the contract's POP start, so this stays accurate even when older base-award transactions have aged out of the load window. - data_refresh_date — USASpending warehouse row-update timestamp. NOT a contractual date; reflects only when the data feed was last refreshed. - is_undefinitized (when present) — True if the latest transaction is a UCA / letter contract that has not yet been definitized. By default, IDV / IDIQ base vehicle rows (which carry zero obligation) are excluded; each row also carries is_idv_base so the consumer can distinguish vehicles from task orders. For live open solicitations, use search_opportunities. For aggregated market statistics, use get_award_summary. For teaming-partner recommendations on a specific bid, use find_teaming_partners.

get_award_summary

ChatGPT
Use this when the user asks for aggregated market statistics over past contract awards — totals, top awardees, year-over-year trends, or breakdowns by set-aside or category. Records are not returned individually; for individual rows use get_award_history. Classification: psc_code filters by what was purchased (use for market sizing and spend analysis). naics_code filters by seller's industry. Both can be combined. For plain-language domain queries ("cybersecurity services", "janitorial"), prefer the category= parameter, which accepts top-level OMB categories and subcategories.

get_award_summary

ChatGPT
Use this when the user asks for aggregated market statistics over past contract awards — totals, top awardees, year-over-year trends, or breakdowns by set-aside or category. Records are not returned individually; for individual rows use get_award_history. Classification: psc_code filters by what was purchased (use for market sizing and spend analysis). naics_code filters by seller's industry. Both can be combined. For plain-language domain queries ("cybersecurity services", "janitorial"), prefer the category= parameter, which accepts top-level OMB categories and subcategories.

get_award_summary

ChatGPT
Use this when the user asks for aggregated market statistics over past contract awards — totals, top awardees, year-over-year trends, or breakdowns by set-aside or category. Records are not returned individually; for individual rows use get_award_history. Classification: psc_code filters by what was purchased (use for market sizing and spend analysis). naics_code filters by seller's industry. Both can be combined. For plain-language domain queries ("cybersecurity services", "janitorial"), prefer the category= parameter, which accepts top-level OMB categories and subcategories.

get_budget_line_items

ChatGPT
Use this when the user wants line-item federal budget data from Congressional Budget Justifications (Greenbook) for the 24 CFO Act agencies including the Department of Defense — to validate budget backing for an opportunity, forecast where new solicitations will emerge, or prioritize pursuits aligned with funded priorities. Filter by agency, fiscal_year, keyword (matches program and justification text), direction ("increase" | "decrease" | "flat"), or min_change_pct. Returns line items with year-over-year funding changes and extracted priority signals. For an aggregate view across all accounts, use get_budget_summary.

get_budget_line_items

ChatGPT
Use this when the user wants line-item federal budget data from Congressional Budget Justifications (Greenbook) for the 24 CFO Act agencies including the Department of Defense — to validate budget backing for an opportunity, forecast where new solicitations will emerge, or prioritize pursuits aligned with funded priorities. Filter by agency, fiscal_year, keyword (matches program and justification text), direction ("increase" | "decrease" | "flat"), or min_change_pct. Returns line items with year-over-year funding changes and extracted priority signals. For an aggregate view across all accounts, use get_budget_summary.

get_budget_line_items

ChatGPT
Use this when the user wants line-item federal budget data from Congressional Budget Justifications (Greenbook) for the 24 CFO Act agencies including the Department of Defense — to validate budget backing for an opportunity, forecast where new solicitations will emerge, or prioritize pursuits aligned with funded priorities. Filter by agency, fiscal_year, keyword (matches program and justification text), direction ("increase" | "decrease" | "flat"), or min_change_pct. Returns line items with year-over-year funding changes and extracted priority signals. For an aggregate view across all accounts, use get_budget_summary.

get_budget_summary

ChatGPT
Use this when the user wants aggregate federal budget trends for an agency across all appropriation accounts — total request vs enacted, net change, count of increasing and decreasing accounts, and the top five accounts by largest increase and largest decrease. For individual line items, use get_budget_line_items.

get_budget_summary

ChatGPT
Use this when the user wants aggregate federal budget trends for an agency across all appropriation accounts — total request vs enacted, net change, count of increasing and decreasing accounts, and the top five accounts by largest increase and largest decrease. For individual line items, use get_budget_line_items.

get_budget_summary

ChatGPT
Use this when the user wants aggregate federal budget trends for an agency across all appropriation accounts — total request vs enacted, net change, count of increasing and decreasing accounts, and the top five accounts by largest increase and largest decrease. For individual line items, use get_budget_line_items.

get_capture_intelligence_bundle

ChatGPT
One-shot capture context: congressional policy intel + agency acquisition forecasts (+ optional recompete contracts). Cross-links by agency, NAICS, and incumbent PIID. Use for questions like DHS border tech, NDAA + pipeline, or recompete + policy together.

get_capture_intelligence_bundle

ChatGPT
One-shot capture context: congressional policy intel + agency acquisition forecasts (+ optional recompete contracts). Cross-links by agency, NAICS, and incumbent PIID. Use for questions like DHS border tech, NDAA + pipeline, or recompete + policy together.

get_capture_intelligence_bundle

ChatGPT
One-shot capture context: congressional policy intel + agency acquisition forecasts (+ optional recompete contracts). Cross-links by agency, NAICS, and incumbent PIID. Use for questions like DHS border tech, NDAA + pipeline, or recompete + policy together.

get_contract_landscape

ChatGPT
Use this when the user asks for a competitive intelligence brief or contract overview for an agency — individual contracts plus market statistics, offers analysis, and pattern detection (sole-source alerts, dominant incumbents, high-competition outliers) in a single response. Supports naics_codes (comma-separated, 4-digit prefix match), min_value (default $2M), modified_within_months (default 15), exclude_vehicles, set_aside_type, min_offers, pop_end_after / pop_end_before (YYYY-MM-DD window).

get_contract_landscape

ChatGPT
Use this when the user asks for a competitive intelligence brief or contract overview for an agency — individual contracts plus market statistics, offers analysis, and pattern detection (sole-source alerts, dominant incumbents, high-competition outliers) in a single response. Supports naics_codes (comma-separated, 4-digit prefix match), min_value (default $2M), modified_within_months (default 15), exclude_vehicles, set_aside_type, min_offers, pop_end_after / pop_end_before (YYYY-MM-DD window).

get_contract_landscape

ChatGPT
Use this when the user asks for a competitive intelligence brief or contract overview for an agency — individual contracts plus market statistics, offers analysis, and pattern detection (sole-source alerts, dominant incumbents, high-competition outliers) in a single response. Supports naics_codes (comma-separated, 4-digit prefix match), min_value (default $2M), modified_within_months (default 15), exclude_vehicles, set_aside_type, min_offers, pop_end_after / pop_end_before (YYYY-MM-DD window).

get_contract_vehicle_info

ChatGPT
Use this when the user asks about federal contract vehicles (GWACs, IDIQs, BPAs, GSA MAS) — to identify competitors on a vehicle, validate vehicle fit for a NAICS, find ordering agencies, or size the market on a vehicle. Tracks 27 vehicles with task-order analytics including SEWP, ITES, OASIS+, Alliant 2, CIO-SP3/SP4, EIS, Polaris, 8(a) STARS III, GSA MAS, SeaPort-NxG, TRICARE, and MHS GENESIS. Pass a specific vehicle name or a sector keyword ("Fed Health", "DoD IT", "NASA GWACs", "GSA GWACs", "NITAAC") for a multi-vehicle rollup. Filter by ordering agency, awardee, or NAICS. Omit vehicle= to list all known vehicles.

get_contract_vehicle_info

ChatGPT
Use this when the user asks about federal contract vehicles (GWACs, IDIQs, BPAs, GSA MAS) — to identify competitors on a vehicle, validate vehicle fit for a NAICS, find ordering agencies, or size the market on a vehicle. Tracks 27 vehicles with task-order analytics including SEWP, ITES, OASIS+, Alliant 2, CIO-SP3/SP4, EIS, Polaris, 8(a) STARS III, GSA MAS, SeaPort-NxG, TRICARE, and MHS GENESIS. Pass a specific vehicle name or a sector keyword ("Fed Health", "DoD IT", "NASA GWACs", "GSA GWACs", "NITAAC") for a multi-vehicle rollup. Filter by ordering agency, awardee, or NAICS. Omit vehicle= to list all known vehicles.

get_contract_vehicle_info

ChatGPT
Use this when the user asks about federal contract vehicles (GWACs, IDIQs, BPAs, GSA MAS) — to identify competitors on a vehicle, validate vehicle fit for a NAICS, find ordering agencies, or size the market on a vehicle. Tracks 27 vehicles with task-order analytics including SEWP, ITES, OASIS+, Alliant 2, CIO-SP3/SP4, EIS, Polaris, 8(a) STARS III, GSA MAS, SeaPort-NxG, TRICARE, and MHS GENESIS. Pass a specific vehicle name or a sector keyword ("Fed Health", "DoD IT", "NASA GWACs", "GSA GWACs", "NITAAC") for a multi-vehicle rollup. Filter by ordering agency, awardee, or NAICS. Omit vehicle= to list all known vehicles.

get_daily_brief

ChatGPT
Use this when the user asks for their morning brief, daily briefing, or "what's happening today" — returns the same unified daily intelligence the user sees on their PrimeRFP Scout dashboard and (when enabled) in their morning email. The brief covers six sections merged across all of the user's discovery scopes — the user does NOT pick a scope — sorted by deadline urgency, posted date, and value: 1. New Opportunities 2. Notable Awards & Recompetes 3. Latest News 4. Funding News 5. Agency News (filtered to scope agencies) 6. Marked Opportunity Updates (changes on watchlisted opportunities — amendments, deadline changes, awards, cancellations) For an agency-wide briefing (not user-personal), use get_agency_briefing instead.

get_daily_brief

ChatGPT
Use this when the user asks for their morning brief, daily briefing, or "what's happening today" — returns the same unified daily intelligence the user sees on their PrimeRFP Scout dashboard and (when enabled) in their morning email. The brief covers six sections merged across all of the user's discovery scopes — the user does NOT pick a scope — sorted by deadline urgency, posted date, and value: 1. New Opportunities 2. Notable Awards & Recompetes 3. Latest News 4. Funding News 5. Agency News (filtered to scope agencies) 6. Marked Opportunity Updates (changes on watchlisted opportunities — amendments, deadline changes, awards, cancellations) For an agency-wide briefing (not user-personal), use get_agency_briefing instead.

get_daily_brief

ChatGPT
Use this when the user asks for their morning brief, daily briefing, or "what's happening today" — returns the same unified daily intelligence the user sees on their PrimeRFP Scout dashboard and (when enabled) in their morning email. The brief covers six sections merged across all of the user's discovery scopes — the user does NOT pick a scope — sorted by deadline urgency, posted date, and value: 1. New Opportunities 2. Notable Awards & Recompetes 3. Latest News 4. Funding News 5. Agency News (filtered to scope agencies) 6. Marked Opportunity Updates (changes on watchlisted opportunities — amendments, deadline changes, awards, cancellations) For an agency-wide briefing (not user-personal), use get_agency_briefing instead.

get_opportunity_changes

ChatGPT
Use this when the user asks what has changed on active solicitations — deadline pushes, status changes, amendments, modifications, or date extensions. A date window applies (maximum 30 days). Two modes: "summary" (default) returns aggregate stats over the window — totals, breakdown by change type, top modified opportunities, and daily change volume. "detail" returns individual change records with field-level diffs and a pre-formatted display string; requires opportunity_uid or change_type.

get_opportunity_changes

ChatGPT
Use this when the user asks what has changed on active solicitations — deadline pushes, status changes, amendments, modifications, or date extensions. A date window applies (maximum 30 days). Two modes: "summary" (default) returns aggregate stats over the window — totals, breakdown by change type, top modified opportunities, and daily change volume. "detail" returns individual change records with field-level diffs and a pre-formatted display string; requires opportunity_uid or change_type.

get_opportunity_changes

ChatGPT
Use this when the user asks what has changed on active solicitations — deadline pushes, status changes, amendments, modifications, or date extensions. A date window applies (maximum 30 days). Two modes: "summary" (default) returns aggregate stats over the window — totals, breakdown by change type, top modified opportunities, and daily change volume. "detail" returns individual change records with field-level diffs and a pre-formatted display string; requires opportunity_uid or change_type.

get_opportunity_detail

ChatGPT
Use this when the user wants the full details for a single opportunity by its UID — contact information, attachments, set-aside, archive date, full description, and AI analysis that are not included in keyword search results. Call this after search_opportunities. Pass the uid= value from the search result row.

get_opportunity_detail

ChatGPT
Use this when the user wants the full details for a single opportunity by its UID — contact information, attachments, set-aside, archive date, full description, and AI analysis that are not included in keyword search results. Call this after search_opportunities. Pass the uid= value from the search result row.

get_opportunity_detail

ChatGPT
Use this when the user wants the full details for a single opportunity by its UID — contact information, attachments, set-aside, archive date, full description, and AI analysis that are not included in keyword search results. Call this after search_opportunities. Pass the uid= value from the search result row.

get_opportunity_summary

ChatGPT
Use this when the user asks for counts, breakdowns, or volumes of currently open solicitations — federal (SAM.gov), state/local/education, and non-federal combined. Examples: "How many cybersecurity contracts are open?", "Breakdown of active IT solicitations at DoD", "How many open RFPs in Virginia?". For individual records, use search_opportunities. For historical award statistics, use get_award_summary. Filter by query (title/description keywords), naics_code, psc_code, agency, set_aside, source_type ("federal" | "sled" | "non-federal" | "all"), location (state/city substring), or posted_within_days (default 90).

get_opportunity_summary

ChatGPT
Use this when the user asks for counts, breakdowns, or volumes of currently open solicitations — federal (SAM.gov), state/local/education, and non-federal combined. Examples: "How many cybersecurity contracts are open?", "Breakdown of active IT solicitations at DoD", "How many open RFPs in Virginia?". For individual records, use search_opportunities. For historical award statistics, use get_award_summary. Filter by query (title/description keywords), naics_code, psc_code, agency, set_aside, source_type ("federal" | "sled" | "non-federal" | "all"), location (state/city substring), or posted_within_days (default 90).

get_opportunity_summary

ChatGPT
Use this when the user asks for counts, breakdowns, or volumes of currently open solicitations — federal (SAM.gov), state/local/education, and non-federal combined. Examples: "How many cybersecurity contracts are open?", "Breakdown of active IT solicitations at DoD", "How many open RFPs in Virginia?". For individual records, use search_opportunities. For historical award statistics, use get_award_summary. Filter by query (title/description keywords), naics_code, psc_code, agency, set_aside, source_type ("federal" | "sled" | "non-federal" | "all"), location (state/city substring), or posted_within_days (default 90).

get_piid_dossier

ChatGPT
Use this when the user provides a federal PIID (prime award or task order number) and wants the full dossier — prime contract record plus subaward totals, optionally with detailed subaward rows. Prefer this over calling get_award_history and find_subawards separately when the user has a specific PIID in hand. Set include_subaward_rows=true to include up to subaward_limit detail rows (default 25, max 100).

get_piid_dossier

ChatGPT
Use this when the user provides a federal PIID (prime award or task order number) and wants the full dossier — prime contract record plus subaward totals, optionally with detailed subaward rows. Prefer this over calling get_award_history and find_subawards separately when the user has a specific PIID in hand. Set include_subaward_rows=true to include up to subaward_limit detail rows (default 25, max 100).

get_piid_dossier

ChatGPT
Use this when the user provides a federal PIID (prime award or task order number) and wants the full dossier — prime contract record plus subaward totals, optionally with detailed subaward rows. Prefer this over calling get_award_history and find_subawards separately when the user has a specific PIID in hand. Set include_subaward_rows=true to include up to subaward_limit detail rows (default 25, max 100).

get_protest_sustain_rates

ChatGPT
Use this when the user wants aggregate sustain / deny / dismiss counts and the merit sustain rate — sustains divided by (sustains + denies) — over published GAO decisions. Uses the same filter set as list_protest_decisions. Covers decided cases only; does not include open or pending matters.

get_protest_sustain_rates

ChatGPT
Use this when the user wants aggregate sustain / deny / dismiss counts and the merit sustain rate — sustains divided by (sustains + denies) — over published GAO decisions. Uses the same filter set as list_protest_decisions. Covers decided cases only; does not include open or pending matters.

get_protest_sustain_rates

ChatGPT
Use this when the user wants aggregate sustain / deny / dismiss counts and the merit sustain rate — sustains divided by (sustains + denies) — over published GAO decisions. Uses the same filter set as list_protest_decisions. Covers decided cases only; does not include open or pending matters.

get_recompete_methodology

ChatGPT
Use this when the user asks why recompete numbers or dates from this app differ from another system they are comparing against — returns the internal methodology guide covering field mapping, PIID aggregation rules, upcoming-vs-historical behavior, and common variance causes. Does not query live award data. For live rows, use find_recompete_contracts or get_award_history.

get_recompete_methodology

ChatGPT
Use this when the user asks why recompete numbers or dates from this app differ from another system they are comparing against — returns the internal methodology guide covering field mapping, PIID aggregation rules, upcoming-vs-historical behavior, and common variance causes. Does not query live award data. For live rows, use find_recompete_contracts or get_award_history.

get_recompete_methodology

ChatGPT
Use this when the user asks why recompete numbers or dates from this app differ from another system they are comparing against — returns the internal methodology guide covering field mapping, PIID aggregation rules, upcoming-vs-historical behavior, and common variance causes. Does not query live award data. For live rows, use find_recompete_contracts or get_award_history.

get_usage_metrics

ChatGPT
Use this when the user wants their real usage metrics for the current calendar month: total tool invocations, remaining calls before the monthly limit, and per-tool call counts with success/error rates and average latency.

get_usage_metrics

ChatGPT
Use this when the user wants their real usage metrics for the current calendar month: total tool invocations, remaining calls before the monthly limit, and per-tool call counts with success/error rates and average latency.

get_usage_metrics

ChatGPT
Use this when the user wants their real usage metrics for the current calendar month: total tool invocations, remaining calls before the monthly limit, and per-tool call counts with success/error rates and average latency.

list_protest_decisions

ChatGPT
Use this when the user wants published GAO bid-protest decisions — individual case rows with decision URLs and (optionally) narrative previews. Covers decided cases only; does not list open or pending protests still before GAO. Pair with find_recompete_contracts and get_award_history to study sustain patterns at an agency. Filter by agency (use "DoD" to match Navy, Army, Air Force, Marine Corps), awardee, protester, naics_code, date range, or narrative_contains (substring on decision text). Set include_narrative=true for a capped preview.

list_protest_decisions

ChatGPT
Use this when the user wants published GAO bid-protest decisions — individual case rows with decision URLs and (optionally) narrative previews. Covers decided cases only; does not list open or pending protests still before GAO. Pair with find_recompete_contracts and get_award_history to study sustain patterns at an agency. Filter by agency (use "DoD" to match Navy, Army, Air Force, Marine Corps), awardee, protester, naics_code, date range, or narrative_contains (substring on decision text). Set include_narrative=true for a capped preview.

list_protest_decisions

ChatGPT
Use this when the user wants published GAO bid-protest decisions — individual case rows with decision URLs and (optionally) narrative previews. Covers decided cases only; does not list open or pending protests still before GAO. Pair with find_recompete_contracts and get_award_history to study sustain patterns at an agency. Filter by agency (use "DoD" to match Navy, Army, Air Force, Marine Corps), awardee, protester, naics_code, date range, or narrative_contains (substring on decision text). Set include_narrative=true for a capped preview.

schedule_discovery_report

ChatGPT
Use this when the user wants to save or update a recurring opportunity-discovery schedule — reports run automatically on the chosen cadence. Set frequency to "daily", "weekly", "biweekly", "monthly", or "never" (to pause). By default, results are stored for later retrieval; set send_email=true to also email them to the user's account address. Set enabled=false to pause without deleting the schedule.

schedule_discovery_report

ChatGPT
Use this when the user wants to save or update a recurring opportunity-discovery schedule — reports run automatically on the chosen cadence. Set frequency to "daily", "weekly", "biweekly", "monthly", or "never" (to pause). By default, results are stored for later retrieval; set send_email=true to also email them to the user's account address. Set enabled=false to pause without deleting the schedule.

schedule_discovery_report

ChatGPT
Use this when the user wants to save or update a recurring opportunity-discovery schedule — reports run automatically on the chosen cadence. Set frequency to "daily", "weekly", "biweekly", "monthly", or "never" (to pause). By default, results are stored for later retrieval; set send_email=true to also email them to the user's account address. Set enabled=false to pause without deleting the schedule.

search_opportunities

ChatGPT
Use this when the user wants to find currently active contracting opportunities — federal (SAM.gov), state/local/education (SLED), or non-federal — or to look up a specific solicitation or contract number. Set source_type to "federal", "sled", "non-federal", or "all" (default). When the user gives any solicitation, contract, order, or RFP/RFQ number, set solicitation_number= and leave query= empty. Otherwise, put subject-only keywords in query= (omit generic words like "opportunity", "solicitation", "active", "federal"). For historical awards or spending, use get_award_history or get_award_summary. For contracts expiring soon, use find_recompete_contracts. For teaming partners on a specific opportunity, use find_teaming_partners. To restrict to a geography, set location= to a state ("MO" or "Missouri"), a city + state ("Kansas City, MO"), or a county + state ("Jackson County, MO"). County filtering requires a state because county names repeat across states.

search_opportunities

ChatGPT
Use this when the user wants to find currently active contracting opportunities — federal (SAM.gov), state/local/education (SLED), or non-federal — or to look up a specific solicitation or contract number. Set source_type to "federal", "sled", "non-federal", or "all" (default). When the user gives any solicitation, contract, order, or RFP/RFQ number, set solicitation_number= and leave query= empty. Otherwise, put subject-only keywords in query= (omit generic words like "opportunity", "solicitation", "active", "federal"). For historical awards or spending, use get_award_history or get_award_summary. For contracts expiring soon, use find_recompete_contracts. For teaming partners on a specific opportunity, use find_teaming_partners. To restrict to a geography, set location= to a state ("MO" or "Missouri"), a city + state ("Kansas City, MO"), or a county + state ("Jackson County, MO"). County filtering requires a state because county names repeat across states.

search_opportunities

ChatGPT
Use this when the user wants to find currently active contracting opportunities — federal (SAM.gov), state/local/education (SLED), or non-federal — or to look up a specific solicitation or contract number. Set source_type to "federal", "sled", "non-federal", or "all" (default). When the user gives any solicitation, contract, order, or RFP/RFQ number, set solicitation_number= and leave query= empty. Otherwise, put subject-only keywords in query= (omit generic words like "opportunity", "solicitation", "active", "federal"). For historical awards or spending, use get_award_history or get_award_summary. For contracts expiring soon, use find_recompete_contracts. For teaming partners on a specific opportunity, use find_teaming_partners. To restrict to a geography, set location= to a state ("MO" or "Missouri"), a city + state ("Kansas City, MO"), or a county + state ("Jackson County, MO"). County filtering requires a state because county names repeat across states.

send_report_email

ChatGPT
Use this when the user wants a report built from session data emailed to themselves. Compose the report HTML (fragment or full document) and pass it as html_content. The body is wrapped in a branded email shell before delivery. Recipient: emails are delivered only to the authenticated user's own account email. If to_email is supplied it must match that address (case-insensitive). Optional parameters support a plain-text summary, up to five additional file attachments, and a short conversation_note label. By default, the email body is a short teaser and the full HTML report is attached as a file for correct rendering; set embed_full_report_in_email_body=true to inline it instead.

send_report_email

ChatGPT
Use this when the user wants a report built from session data emailed to themselves. Compose the report HTML (fragment or full document) and pass it as html_content. The body is wrapped in a branded email shell before delivery. Recipient: emails are delivered only to the authenticated user's own account email. If to_email is supplied it must match that address (case-insensitive). Optional parameters support a plain-text summary, up to five additional file attachments, and a short conversation_note label. By default, the email body is a short teaser and the full HTML report is attached as a file for correct rendering; set embed_full_report_in_email_body=true to inline it instead.

send_report_email

ChatGPT
Use this when the user wants a report built from session data emailed to themselves. Compose the report HTML (fragment or full document) and pass it as html_content. The body is wrapped in a branded email shell before delivery. Recipient: emails are delivered only to the authenticated user's own account email. If to_email is supplied it must match that address (case-insensitive). Optional parameters support a plain-text summary, up to five additional file attachments, and a short conversation_note label. By default, the email body is a short teaser and the full HTML report is attached as a file for correct rendering; set embed_full_report_in_email_body=true to inline it instead.

setup_ebuy_notifications

ChatGPT
Use this when the user wants to set up or manage GSA eBuy RFQ notification forwarding — parsed, classified, and triaged automatically based on their profile. Set enabled=true to activate, false to pause. Use filter_rules for natural-language criteria (e.g. "only NAICS 541512, DoD agencies, minimum $500K"). Set digest_frequency to "immediate", "daily", "weekly", or "none" (in-app only).

setup_ebuy_notifications

ChatGPT
Use this when the user wants to set up or manage GSA eBuy RFQ notification forwarding — parsed, classified, and triaged automatically based on their profile. Set enabled=true to activate, false to pause. Use filter_rules for natural-language criteria (e.g. "only NAICS 541512, DoD agencies, minimum $500K"). Set digest_frequency to "immediate", "daily", "weekly", or "none" (in-app only).

setup_ebuy_notifications

ChatGPT
Use this when the user wants to set up or manage GSA eBuy RFQ notification forwarding — parsed, classified, and triaged automatically based on their profile. Set enabled=true to activate, false to pause. Use filter_rules for natural-language criteria (e.g. "only NAICS 541512, DoD agencies, minimum $500K"). Set digest_frequency to "immediate", "daily", "weekly", or "none" (in-app only).

Capabilities

InteractiveWrites

Example Prompts

Click any prompt to copy it.

App Stats

96

Tools

4

Prompts

ChatGPT

Platforms

Works with

ChatGPT

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