MCP App Store
by KeenEthics
Productivity
GovQuery icon

GovQuery

by Program Integrity Alliance (PIA)

Overview

GovQuery is a powerful search engine that makes U.S. government reports and data easier to explore, locate, and understand. Built by the Program Integrity Alliance, GovQuery processes over 300,000 open access articles, reports, and oversight recommendations to make them more digestible for AI platforms — offering deeper, more targeted search than general-purpose engines. Every result links to the exact page where content appears, and you can filter by granular categories unavailable on the source platforms. Data sources currently include: - Reports from the Government Accountability Office (GAO), Federal Inspectors - General (Oversight.gov), and the Congressional Research Service (CRS) - Annual reports from Chief Financial Officers (CFO) Act of 1990 agencies — Agency Financial Reports (AFRs), Performance & Accountability Reports(PFRs), and Congressional Budget Justifications (CJs) since 2019 - Department of Justice press releases - Executive orders (last six presidents) GovQuery also includes all open oversight recommendations from the GAO and Federal Inspectors General (Oversight.gov), with tools to analyze them by category and track trends over time. New data sources are added regularly. For more information, see our FAQ (https://www.programintegrity.org/faq)

Tools

fetch

ChatGPT
Retrieve the full contents of a specific document from the PIA database using its unique identifier. This endpoint is one of the supported for OpenAI's MCP spec when integrating ChatGPT Connectors.

pia_oversight_recommendations

ChatGPT
Search oversight recommendations (Open Recommendations dataset) with facets enabled by default. Today's date is 2026-06-22. This dataset contains recommendations from GAO and Oversight.gov only — SourceDocumentDataSource filters for any other source will be ignored. Do NOT filter by SourceDocumentDataSet — this tool already targets the 'Open Recommendations' dataset automatically. IMPORTANT: When a text query is provided, do NOT add a referenced_agencies filter — the query handles relevance matching. Only use referenced_agencies when the query is empty (pure filter/facet lookups). IMPORTANT — your response MUST: 1) Cite total_count as the number of OPEN recommendations matching the query AND every filter clause. 2) Summarize the recommendations in results; you MAY break the set down using facets (status, priority, agency, theme, …). 3) Caveat totals from TEXT queries: semantic matching is approximate; pure filter/agency lookups are exact. 4) ALWAYS include a Find Out More section linking the govquery_url so the user can open the full set in Rec Spotlight. Recommendations have NO per-document citations — do NOT fabricate [[N]](url) citations or a References section; direct users to Rec Spotlight via govquery_url.

pia_oversight_recommendations

Claude

App Stats

6

Tools

ChatGPT, Claude

Platforms

Works with

ChatGPT
Claude

Data refreshed daily