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Glama

Fac

by io.github.pipeworx-io

Fac Get Audit

fac_get_audit
Read-onlyIdempotent

Fetch 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 the entity expended money under, the program name, dollars expended per program, whether it was audited as a major program, and the opinion type. Also returns auditee contact and auditor details, plus audit findings when the FAC findings table carries them. Answers "what did this specific audit cover and what did it conclude".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
report_idYesFAC report id as returned by fac_search_audits, e.g. "2023-06-CENSUS-0000250449". Aliases: id, audit_id.
award_limitNoMax federal award rows to include, 1-500 (default 200).
include_findingsNoAttach audit findings for this report (default true).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "award_limit": 3,
-    "report_id": "2023-08-GSAFAC-0000039233"
-  }
-]New value: +[
+  {
+    "award_limit": 25,
+    "report_id": "2024-08-GSAFAC-0000063952"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds genuine behavioral context beyond that: findings are included only 'when the FAC findings table carries them,' and the tool enriches the base audit record with federal program detail. It is transparent about conditional content and scope without contradicting the annotations.

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?

The description is long but each clause adds relevant detail about the returned data; it is front-loaded with the primary purpose. The final sentence, 'Answers what did this specific audit cover and what did it conclude,' is somewhat redundant with the preceding enumeration, but overall the length is justified by the tool's complexity.

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 carries the burden of explaining return values, and it does so thoroughly: summary_url, per-program CFDA numbers and expenditures, major program status, opinion type, auditee/auditor details, and conditional findings. It does not explicitly describe the result shape or error behavior, but for a read-only fetch tool the listed fields are sufficient guidance.

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 structured schema already documents all three parameters. The tool description reinforces report_id with a concrete example and clarifies the 'single audit' semantics, but it does not materially expand on award_limit or include_findings beyond what the schema already provides. Baseline 3 is appropriate.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'Fetch one single audit by its FAC report_id', and enumerates the key payload fields (summary_url, CFDA numbers, program names, expenditures, major program status, opinion type). This clearly distinguishes it from sibling tools like fac_search_audits (searching) and fac_audit_findings (findings-only), even though siblings are not named explicitly.

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

Usage Guidelines4/5

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

The description makes the use case clear: call this when you have a specific FAC report_id and need the full audit record with program-level and finding detail. It does not explicitly state when not to use it or name alternatives, but the 'one single audit by report_id' phrasing strongly implies the retrieval pattern and the schema's reference to fac_search_audits adds context.

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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TDQS

A3.9/5.0
Disambiguation2/5

Several tools occupy the same "answer a factual question" niche: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route to the same underlying catalog, so an agent must parse subtle differences to pick correctly. scan_competitor_ai_presence also wraps ai_visibility_check, adding another near-duplicate. The detailed descriptions help, but the boundaries are genuinely fuzzy.

Naming Consistency3/5

The set mixes several conventions: fac_*, polymarket_*, and pipeworx_* prefixes coexist with bare verbs (remember, recall, forget, subscribe, unsubscribe) and noun phrases (entity_profile, recent_changes, bet_research). ask_pipeworx_beta/grounded use a suffix pattern while pipeworx_feedback/trending use a prefix, so there is no single predictable scheme. Still, most names are readable and describe what they do.

Tool Count2/5

36 tools is well above the 25+ threshold and creates a heavy surface for any client to load and reason about. The broad data-platform scope explains some of the count, but many tools are meta-variants of the same query/research capability rather than genuinely distinct operations.

Completeness4/5

For the server's apparent purpose—authoritative data lookup, research, prediction-market analysis, and account/feed management—the surface covers the core lifecycle: query, entity resolution, profiles, comparisons, recent changes, claim verification, subscriptions, alerts, and memory. Minor gaps exist (no direct tool to fetch a pipeworx:// citation URI; no raw per-pack access), but most workflows are supported.

Resources