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Fac Search Audits

fac_search_audits
Read-onlyIdempotent

Search 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, school districts, fire and water districts, housing authorities, universities, tribes, hospitals and nonprofits. Returns the report_id, summary_url (the official FAC dissemination summary page for that submission), the organization name, city, state and ZIP on file, total federal awards expended for the year, the cognizant/oversight agency, and the date FAC accepted the submission. Puerto Rico municipalities are found under either their Spanish or English legal name. Answered without a live api.data.gov key or its rate limit. Answers "does this grant recipient file a single audit and how much federal money does it run", and "what is the FAC report ID / summary URL for this entity".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
einNoEmployer Identification Number, digits only, exact match. Alias: auditee_ein.
ueiNoSAM.gov Unique Entity Identifier (12 chars), exact match. Alias: auditee_uei.
zipNoAuditee ZIP code, matched on the 5-digit prefix so it works against both the 5-digit and 9-digit forms FAC stores ("75082", "009601588"). An auditee's ZIP on file changes between filing years; if it filters everything out the tool says which ZIPs FAC actually holds. Aliases: auditee_zip, zipcode, zip_code, postal_code.
limitNoRows to return, 1-200 (default 20).
stateNo2-letter auditee state code (e.g. "CA"). Alias: auditee_state.
offsetNoRows to skip, for paging.
order_byNoSort column, descending by default. Defaults to total_amount_expended.
ascendingNoSort ascending instead of descending.
audit_yearNoFiscal audit year, e.g. 2023.
auditee_nameNoOrganization name, matched as a forgiving case-insensitive substring (e.g. "stanford", "county of los angeles"). A municipality name is searched in both languages FAC files under, so "MUNICIPIO DE BAYAMON" and "Municipality of Bayamon" reach the same rows. Aliases: query, q, name.
audit_year_minNoEarliest audit year to include.
min_total_expendedNoOnly audits whose total federal awards expended is at least this many dollars (e.g. 100000000 for billion-dollar-scale recipients).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "auditee_name": "stanford",
-    "limit": 10
-  },
-  {
-    "audit_year": 2024,
-    "limit": 10,
-    "min_total_expended": 100000000,
-    "state": "CA"
-  }
-]New value: +[
+  {
+    "auditee_name": "stanford",
+    "limit": 10
+  },
+  {
+    "audit_year": 2024,
+    "limit": 10,
+    "min_total_expended": 100000000,
+    "state": "CA"
+  },
+  {
+    "auditee_name": "City of Richardson",
+    "limit": 5,
+    "state": "TX",
+    "zip": "75082"
+  },
+  {
+    "auditee_name": "MUNICIPIO DE BAYAMON",
+    "limit": 5
+  }
+]
  2. Changed2 schema fields changed
    • changedInput schema / properties / auditee_name / description
      Previous value: -"Organization name, matched as a forgiving case-insensitive substring (e.g. \"stanford\", \"county of los angeles\"). Aliases: query, q, name."New value: +"Organization name, matched as a forgiving case-insensitive substring (e.g. \"stanford\", \"county of los angeles\"). A municipality name is searched in both languages FAC files under, so \"MUNICIPIO DE BAYAMON\" and \"Municipality of Bayamon\" reach the same rows. Aliases: query, q, name."
    • addedInput schema / properties / zip
      Added value: +{
+  "description": "Auditee ZIP code, matched on the 5-digit prefix so it works against both the 5-digit and 9-digit forms FAC stores (\"75082\", \"009601588\"). An auditee's ZIP on file changes between filing years; if it filters everything out the tool says which ZIPs FAC actually holds. Aliases: auditee_zip, zipcode, zip_code, postal_code.",
+  "type": "string"
+}
  3. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "audit_year": 2023,
-    "auditee_name": "stanford",
-    "limit": 3
-  }
-]New value: +[
+  {
+    "auditee_name": "stanford",
+    "limit": 10
+  },
+  {
+    "audit_year": 2024,
+    "limit": 10,
+    "min_total_expended": 100000000,
+    "state": "CA"
+  }
+]
  4. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower, yet the description adds real operational context: no live api.data.gov key needed and no rate limit applies, and it discloses fallback behavior when a ZIP filter empties the result. Return-field disclosure is also provided. Missing only pagination/result-cap behavior, which the schema covers.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Long but front-loaded: the first clause names the corpus, then filters, then return fields, then nuances. Most sentences earn their place, though the recipient-type enumeration ('cities, towns, counties, school districts, fire and water districts, housing authorities, universities, tribes, hospitals and nonprofits') and the Puerto Rico note are slightly padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly compensates by naming the returned fields (report_id, summary_url, organization, city/state/ZIP, total federal awards expended, cognizant/oversight agency, FAC acceptance date). For a 12-parameter read tool with full schema coverage and rich annotations, this is close to complete; sibling differentiation is the main residual gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3 and the schema already documents every parameter including the ZIP prefix match, substring-name matching, alias lists, and enum values. The description's parameter notes (UEI, EIN, state, ZIP, audit year, minimum dollars) restate rather than extend that, adding little beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Search) and a precisely scoped resource (US single audits filed with the Federal Audit Clearinghouse under Uniform Guidance / A-133), and enumerates the queryable attributes. It never distinguishes itself from the six sibling FAC tools (fac_get_audit, fac_recipient_audit_history, fac_audit_findings, etc.), so an agent cannot tell from the text alone which of these to pick for a given FAC question.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through the two exemplar questions it answers ('does this recipient file a single audit and how much federal money does it run', 'what is the FAC report ID / summary URL'), which is genuinely useful framing. However it gives no explicit when-not-to-use conditions and names no alternative, despite six FAC-family siblings whose overlap is a real routing risk.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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