GovQuery

Retrieve the full contents of a specific PIA document by its unique identifier (the `id` returned by `search`). This tool exists only to satisfy OpenAI's MCP search/fetch spec — use it ONLY alongside `search` when serving ChatGPT Deep Research or Company Knowledge. For all other queries, use `pia_search` / `pia_oversight_recommendations` instead.

Search oversight recommendations (Open Recommendations dataset) with facets enabled by default. Today's date is 2026-07-29. 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). `total_count` is the number of OPEN recommendations matching the query and all applied filters — report it as that count. The `results` are a representative sample to summarize, and `facets` break the set down by status, priority, agency, theme, and GAO topic. Counts from free-text queries are approximate (semantic matching); pure filter or agency lookups are exact. Recommendations have no per-document citations, so it is very important to always include the `govquery_url` for the full set in Rec Spotlight rather than citing individual rows.

Search the Program Integrity Alliance (PIA) database of government oversight reports, recommendations, executive orders, legislation, and integrity data (GAO, OIG/Oversight.gov, CRS, DOJ, Congress.gov, Federal Register). One tool for all document search: scope by source/dataset/agency/date via filter, choose content vs titles via search_mode, sweep every source with wide, or discover filter values with facets_only. WHAT THE COUNTS MEAN: in 'content' mode (default) total_count and facet counts are TEXT CHUNKS / EXCERPTS — a single document is split into many chunks, so these counts are much larger than the number of documents and must NOT be reported as a document/report/article count. In 'titles' mode the counts are whole DOCUMENTS / ARTICLES. Use search_mode='titles' whenever the user asks how many documents, reports, or articles there are. Citations are very important to PIA users, so it is strongly expected that your answer: (1) gives every factual claim at least one clickable inline citation in the form [[1]](url), numbered sequentially from 1; (2) ends with a References section listing every source you cited, each reference on its own line (never multiple references on one line) — omit this section only for a pure count or summary with nothing specific to cite; and (3) includes a Find Out More section with the `govquery_url` so the user can open the full result set. Please do not present findings from these results without their inline citations and References section. Counts from free-text queries are approximate, because semantic search matches variations of the search terms.

Search the Program Integrity Alliance (PIA) GovQuery database of U.S. government oversight material (GAO, Inspectors General / Oversight.gov, CRS, DOJ, Congress.gov, Federal Register), returning relevant results with titles, snippets, and URLs for citation. This tool exists only to satisfy OpenAI's MCP search/fetch spec — use it ONLY when serving ChatGPT Deep Research or Company Knowledge, which require these exact tool signatures. For all other / general PIA queries, use `pia_search` instead (or `pia_oversight_recommendations` for open federal recommendations), which return richer, purpose-built results.