Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Federal Audit Clearinghouse (FAC) MCP — US single-audit data, served from a served from a…
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
PIPEWORX_API_KEY비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://gateway.pipeworx.io/fac/mcpClaude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"fac": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-fac"
],
"env": {
"PIPEWORX_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add --transport http fac https://gateway.pipeworx.io/fac/mcpclaude mcp add fac --env PIPEWORX_API_KEY=your-value -- npx -y @pipeworx/mcp-fac실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"fac": {
"url": "https://gateway.pipeworx.io/fac/mcp"
}
}
}프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"fac": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-fac"
],
"env": {
"PIPEWORX_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"fac": {
"type": "http",
"url": "https://gateway.pipeworx.io/fac/mcp"
}
}
}프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"fac": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-fac"
],
"env": {
"PIPEWORX_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://gateway.pipeworx.io/fac/mcp메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
도구 이름과 설명은 서버가 2026년 11월 6일에 알려 준 그대로예요. 우리가 아니라 제작자가 쓴 글이고, 일반 텍스트로만 보여 드려요.
ask_pipeworxPREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring au
ask_pipeworx_betaBeta version of ask_pipeworx: identical universal router (same 7,176 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so
ask_pipeworx_groundedHallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 7,176 across 1863 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbati
search_withinSemantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into th
deep_researchACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1863 STRUCTURED data sources (SEC filings,
discover_toolsFind tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real est
resolve_entity"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools re
compare_entities"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing
subscribeCreate a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = o
unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
recent_alertsPull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp).
entity_profile"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over cha
recent_changes"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings s
validate_claim"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is f
company_factsTYPED, DETERMINISTIC financial facts for a US public company for an EXPLICITLY NAMED reporting period — "Apple revenue for fiscal 2023", "Walmart net income FY2026 Q3", "Microsoft cash at the end of fiscal 2024". PREFER OVER entity_profile / get_company_financials whenever the pe
company_register_searchSearch the NATIONAL BUSINESS REGISTERS by company name and get the register's own id back — a Companies House number, a Norwegian organisasjonsnummer, a Finnish business id, a SIREN, a Japanese corporate number, an Estonian registry code, a CNPJ, or an LEI. Use this when you want
scan_dependencyComposite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular
bet_researchResearch a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-kristi-noem-win-the-2028-republican-presidential-nomination"), a polymarket.com URL, or a question text. A dated slug ("...-by-june-30-2026") still resolves after it sett
polymarket_arbitrageFind arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `ev
polymarket_edgesScan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segme
polymarket_kalshi_spreadCross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says s
polymarket_fill_riskRealizable-vs-theoretical edge check against live CLOB order-book depth — AND Polymarket's own taker fee, which is never zero and is folded in rather than left for you to add (fleet #3446: r/PredictionsMarkets flagged a "clean" fill that quietly omitted it as optimistic in the di
polymarket_edge_trackerEdge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: d
kalshi_weather_edgePrices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob
resolution_auditExtract the settlement clause of a single Polymarket (.com or .us) or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at (falling back to the market TITLE when the clause itself says "the time range specified in the title" — e.g. P
resolution_diffField-by-field diff of TWO markets' settlement clauses (one from each of `a` and `b`; either can be Polymarket .com, Polymarket US, or Kalshi) — runs resolution_audit on both sides and compares source, settle time, precision, comparison_basis, and evidence standard. Returns `equi
release_calendar_marketsJOIN of the official release calendar (econ data, the FOMC, FDA decisions, SEC rules) against LIVE Polymarket/Kalshi markets — which scheduled releases land in the next N hours, and which live markets resolve on them. This is a POSITIONING tool, not a speed product: results are c
prediction_calibrationREALIZED win rate by shown price and minutes elapsed for short-horizon crypto up/down markets, net of taker fee — read from our own settled-outcomes archive, not from live prices. Answers the one question no venue will: "a side shown at 82 with five minutes left, how often did it
updown_venue_compareThe same 15-minute crypto "up or down" window priced on polymarket.com, Polymarket US and Kalshi, side by side, with whether each pair is actually the same contract. Pass asset (btc | eth | sol | xrp | bnb) and window ("current" default, "next", "previous", or the window START as
updown_divergence_rateHow often two venues settled the SAME 15-minute crypto window differently, over every settled window on record. Compares polymarket.com (Chainlink TWAP) with Kalshi and with Polymarket US (both CF Benchmarks BRTI), and Kalshi with Polymarket US as the same-feed control. Per pair:
archive_evidenceWhat did a web page say on a given date? Finds the archived capture of a URL nearest that date across the Internet Archive Wayback Machine, Common Crawl and Arquivo.pt, reads it, and returns the readable text (HTML stripped) with the capture timestamp, a link to the archived copy
archive_compareWhat changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and b
research_rowClaygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code in
classify_rowCheap categorization: given a company and a list of candidate labels, decide which labels apply — the most common GTM/AI usage shape (tagging a list by industry, ICP fit, etc.), one small model call. Same evidence contract as research_row: each label comes back {label, applies, e
pipeworx_trendingWhat other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice
suggest_questionsWhat can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed e
generate_llms_txtGenerate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at si
ai_visibility_checkProbe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns pe
scan_competitor_ai_presenceCompare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our co
pipeworx_feedbackTell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipewor
rememberSave data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-v
recallRetrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous
forgetDelete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
fac_search_auditsSearch US single audits (Uniform Guidance / former OMB A-133) filed with the Federal Audit Clearinghouse — "the FAC" — by auditee name, UEI, EIN, state, ZIP code, audit year, or minimum federal dollars expended. Covers every kind of non-federal recipient: cities, towns, counties,
fac_get_auditFetch one single audit by its FAC report_id (e.g. "2025-09-GSAFAC-0000422512"), including its summary_url — the official Federal Audit Clearinghouse dissemination summary page for that report — enriched with the full federal program detail: every Assistance Listing (CFDA) number
fac_audit_findingsAudit findings recorded against single audits — the compliance exceptions, material weaknesses, significant deficiencies, questioned costs and repeat findings that auditors reported. Query by report_id, or by recipient name/UEI to sweep every audit that recipient filed. Answers "
fac_federal_awards_by_programList the organizations that expended money under a given federal Assistance Listing / CFDA number (e.g. "93.224" community health centers, "84.010" Title I, "20.205" highway planning), ranked by dollars expended, with the recipient name, state, audit year and whether it was a maj
fac_recipient_audit_historyYear-by-year single-audit history for one grant recipient, identified by UEI or by name. Returns each audit year with total federal awards expended, the report_id, the oversight agency and the FAC acceptance date, plus a growth read on the spend trend. Answers "how has this organ
fac_findings_searchFull-text search over audit finding NARRATIVES — the actual prose auditors wrote describing a compliance exception ("failed to submit the Federal Single Audit report within the timeframe prescribed", "total available cash was less than total advances from grantors"). This is not
fac_cross_table_screenScreen single audits across state, federal program (CFDA), and finding type / material weakness in ONE call — a join FAC's own live API cannot express (its PostgREST has no foreign key between /general, /federal_awards and /findings). Answers compound diligence questions like "ma
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 pipeworx-io 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
192ms 만에 응답(HTTP 200)
로그인 필요 없음
도구 설명 51개를 읽었고, AI에게 보내는 숨겨진 지시는 없었어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 3개 중 3개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: PIPEWORX_API_KEY
둘 다 가능해요. 내 컴퓨터에 설치해도 되고, 클라우드 버전에 연결해도 돼요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.2.1)은 5일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Intervals MCP
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Remote MCP server for intervals.icu run data and analysis
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Federal Audit Clearinghouse MCP — US single-audit filings (Uniform Guidance, formerly OMB A-133): who spends federal grant money, under which Assistance Listing program, and what the auditors found. Backed by a weekly-refreshed copy of FAC's own bulk data (fleet #2504) — no API key needed.
Part of Pipeworx — an MCP gateway connecting AI agents to 1864+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.
fac_search_audits(auditee_name|uei|ein|state|zip|audit_year|min_total_expended, ...) — find audits; returns report_id, organization identity (including the ZIP on file), total federal awards expended, oversight agency, FAC acceptance date.fac_get_audit(report_id, ...) — one audit plus every federal program row on its Schedule of Expenditures of Federal Awards (Assistance Listing number, program name, dollars expended, major-program flag, opinion type), auditor firm, and findings when FAC carries them.fac_audit_findings(report_id | auditee_name | uei, ...) — findings recorded against an audit, or swept across every audit one recipient filed, rolled up into a severity summary (material weaknesses, significant deficiencies, questioned costs, repeat findings, modified opinions). include_text attaches the narrative finding text.fac_federal_awards_by_program(cfda | federal_agency_prefix + federal_award_extension, ...) — who expended money under a CFDA number (e.g. 93.224), ranked by dollars, joined to recipient name/state/year.fac_recipient_audit_history(uei | auditee_name | ein, ...) — one recipient's audits year by year with total expended per year and a percent-change read on the trend.fac_findings_search(query, ...) — NEW. Full-text search over finding narratives ("failed to submit... within the timeframe prescribed", "questioned costs"). Not expressible against the live FAC API at all (no cross-table join); backed by a Postgres websearch_to_tsquery RPC (fac_findings_search) over a GIN-indexed tsvector column.fac_cross_table_screen(state | cfda | finding_type | material_weakness_only | min_amount | audit_year, ...) — NEW. Screens audits across state, CFDA program and finding type in one call ("material weaknesses in Texas under CFDA 93.224"). Backed by a 3-way SQL join RPC (fac_cross_table_screen) across fac_general + fac_federal_awards + fac_findings.Forgiving aliases throughout: query / q / name for auditee_name; zip / auditee_zip / zipcode / zip_code / postal_code; cfda / program_number / assistance_listing / program all accept "93.224", "93-224", "93224", or a bare "93" for every program at that agency.
An argument this pack does not recognize is reported, not dropped. Any unknown key comes back in ignored_arguments with a warnings line saying the filter was not applied and listing what the tool does accept. Silently discarding the most specific thing a caller said is worse than erroring, because the response still looks like an answer.
Every response carries `data_as_of` — the timestamp of the last successful refresh (from fac_ingest_runs). Before the first refresh completes it is null and the response adds a coverage_status note saying so, rather than silently returning an empty result that looks like "no audits exist".
This pack used to call api.fac.gov (a PostgREST API fronted by the api.data.gov umbrella) live on every request. Measured 60 days to 2026-09-29: fac_search_audits was upstream_down on 572 of ~1,620 external calls (35%, 3.9s average latency) — the single largest ask_pipeworx no-match cluster in the catalog (roughly 25 distinct question shapes: name+state+ZIP variants, report_id lookups, "audits with findings about X"). Separately, api.fac.gov's own PostgREST exposes no foreign key between `/general` and `/findings` (PGRST200 on any embed attempt), so a cross-table question like "material weaknesses in Texas under CFDA 93.224" was structurally impossible against the live API, not merely slow.
Per Bruce's 2026-09-23 ruling ("build a copy when live doesn't serve / is too slow"), the pack now reads its own periodically-refreshed table instead. Refreshed weekly by scripts/fac-upsert.sh, scheduled by .github/workflows/fac-refresh.yml. Schema + RPCs: supabase/migrations/220_fac_local_mirror.sql.
Tables loaded: general, federal_awards, findings, findings_text, corrective_action_plans (as fac_general, fac_federal_awards, fac_findings, fac_findings_text, fac_corrective_action_plans). NOT loaded yet (out of #2504's scope, a future extension, not a silent gap): notes_to_sefa (725MB), passthrough (559MB), and the small additional_ueis/additional_eins/secondary_auditors tables.
FAC's general.csv extract carries named-individual contact fields — auditee/auditor CERTIFY name+title, CONTACT name+title, email, phone. None of those are loaded. Organizational identity (auditee_name, auditor_firm_name, addresses, EINs) is kept — it identifies the entity filing a mandatory federal compliance disclosure, not a private individual, and is the same class of data the samgov and fed-nic packs already carry. fac_get_audit used to also surface auditee contact name/email/phone when proxying the live API (GENERAL_CONTACT_SELECT); that capability is intentionally dropped, not degraded — every response from fac_get_audit carries a personal_data_note pointing to the report's summary_url on fac.gov instead. See the migration and scripts/fac-normalize.py headers for the full column-by-column reasoning.
node scripts/fac-upsert.sh # local dry run (needs the database connection secret in env — see the script header)
In production this runs on a GitHub Actions runner (.github/workflows/fac-refresh.yml, weekly, workflow_dispatch-able), not the CF gateway Worker — federal_awards alone is ~1.34GB uncompressed, far past a Worker's execution budget, and psql \copy does the load in one shot where a Worker's streaming parser could not (same reasoning as the samgov and openfema packs). The loader:
*_fac_*.sql migration (idempotent — no separate "apply once by hand" step to forget).https://app.fac.gov/dissemination/public-data/gsa/full/{table}.csv (follows the 302 to a presigned S3 URL that expires in ~30s — never resolve and reuse the Location header, same trap as migration 135's samgov table).scripts/fac-normalize.py — streams row-by-row (never buffers a whole file in memory), drops the personal-data columns, and NULLs the literal "GSA_MIGRATION" sentinel FAC's own data uses as a placeholder for finding text/corrective-action-plan text on records migrated from the pre-2022 Census-run FAC (verified live 2026-09-29 — thousands of 2016-era report_ids carry that literal string as if it were content).psql \copy, then upserts into the live table in batches (never a single giant transaction).fac_ingest_runs (status, row counts, finished_at) — this is what data_as_of reads.https://www.fac.gov/data/download/current/ — the human-readable index of bulk extracts.https://app.fac.gov/dissemination/public-data/gsa/full/{general,federal_awards,findings,findings_text,corrective_action_plans}.csv — the actual downloads (302 → presigned S3, no credential). Sizes observed 2026-09-29: general 271MB, federal_awards 1.34GB, findings 68MB, findings_text 258MB, corrective_action_plans 110MB.ZIP is stored in two encodings and must be prefix-matched. auditee_zip holds 5 digits (75082) for some rows and 9-digit ZIP+4 with no separator (009601588, 750814198) for others, so eq. misses most of the table — a Puerto Rico caller passing 00960 would match nothing at all. The pack filters on the 5-digit prefix (auditee_zip=like.00960*), which is the only part both encodings share, and accepts ZIP+4 input by truncating it.
An auditee's ZIP on file changes between filing years. CITY OF RICHARDSON TX is filed under 75080 (2018–2021), 75083 (2022) and 75082 (2025) — all the same UEI. So a current ZIP legitimately fails to match an older audit. When a zip filter empties an otherwise-matching result, the pack re-asks without it and returns reason: 'zip_no_match' naming the ZIPs FAC actually holds for that entity, rather than a bare miss the caller cannot diagnose.
Puerto Rico municipalities are filed in BOTH languages, and which one depends on the submission year. FAC holds MUNICIPALITY OF BAYAMON and MUNICIPALITY OF SAN JUAN, but also MUNICIPIO DE MANATI, MUNICIPIO DE CAMUY, MUNICIPIO DE NARANJITO — and Corozal appears as MUNICIPIO DE COROZAL for 2019 and MUNICIPALITY OF COROZAL for 2017–2024, from the same entity. A caller working from a Puerto Rico government source has the Spanish legal name, which no substring of the English row contains; a one-way rewrite to English would have lost the Spanish rows instead. The pack detects a municipality name in either language (MUNICIPIO DE X, MUNICIPIO AUTONOMO DE X, MUNICIPALIDAD DE X, (AUTONOMOUS) MUNICIPALITY OF X), extracts the place, and queries both spellings in one PostgREST or=(...), echoing what it did in name_match.
Place names carry their Spanish accents, inconsistently. FAC holds MUNICIPALITY OF AÑASCO (and in one row the mojibaked MUNICIPALITY OF A?ASCO) *and* the plain MUNICIPIO DE ANASCO, so neither spelling finds all of them: ASCII ANASCO returns 1 audit, accented AÑASCO returns 4, and the entity has 5. Each place pattern therefore also goes out accent-blind, with the letters that can carry a Spanish accent replaced by LIKE's single-character wildcard (Municipality of ___SC_); the surviving consonants and the fixed length keep it specific.
This rides in the primary query rather than as a retry-on-empty, and that distinction is the whole point: a retry only fires when the literal spelling found *nothing*, but the actual failure is that it finds *some* — a partial answer wearing the shape of a complete one. or=(...) is a single SQL predicate, so the extra branches union without duplicating rows or costing a second round trip.
`federal_agency_prefix` and `federal_award_extension` are stored separately and only mean something concatenated: prefix 93 + extension 224 is Assistance Listing 93.224, so callers who pass the dotted number get it split for them and every response echoes assistance_listing back. The extension is stored zero-padded to three characters (47.076, never 47.76), so a numeric extension is padded before it is sent — an unpadded one matches nothing rather than erroring. `fac_federal_awards` carries no auditee columns — recipient name, state and audit year live on fac_general, so fac_federal_awards_by_program joins the two by report_id; when a state or audit_year filter is set it over-fetches the top rows by dollar amount and filters after the join, and reports rows_scanned so a caller can tell a deeper scan is needed. Zero findings is an answer, not a gap: a clean single audit legitimately has no rows in fac_findings, which is returned as found: false, reason: 'no_findings' with the audits that were checked, distinct from reason: 'findings_table_unavailable'. Entities spen
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요