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by io.github.pipeworx-io
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Fac Recipient Audit History

fac_recipient_audit_history
Read-onlyIdempotent

Year-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 organization's federal funding moved over time and which years were audited".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
einNoEmployer Identification Number, an alternative exact identifier. Alias: auditee_ein.
ueiNoSAM.gov Unique Entity Identifier, the precise way to pin one recipient. Alias: auditee_uei.
stateNo2-letter state code, useful to disambiguate a common name.
yearsNoMax audit years to return, 1-40 (default 15).
auditee_nameNoRecipient name substring when the UEI is unknown, e.g. "johns hopkins". Aliases: query, q, name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "uei": "HJD6G4D6TJY5"
-  }
-]New value: +[
+  {
+    "uei": "HJD6G4D6TJY5",
+    "years": 5
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations (readOnlyHint, idempotentHint, etc.) already disclose safety and idempotency. The description adds value by specifying output fields and the growth read, but does not mention data freshness, rate limits, or error handling. Overall, behavioral context is adequate.

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

Conciseness5/5

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

Two sentences plus a concluding line, each purposeful. The first sentence states the core function, the second details output, and the third captures the user's intent. No wasted words.

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?

Given no output schema, the description compensates by enumerating returned fields (audit year, total federal awards, report_id, oversight agency, FAC acceptance date, spend trend growth). It also notes identification methods. However, it omits details like default years value or state usage, which are in the schema but not reinforced in the text.

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?

Input schema has 100% description coverage for all 5 parameters, so baseline is 3. The description does not add additional parameter meaning beyond the schema (e.g., it does not explain how 'state' disambiguates names or default for 'years').

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?

Description clearly states it returns year-by-year single-audit history for one grant recipient, identifies allowed methods (UEI or name), and lists specific output fields. It distinguishes from siblings like fac_search_audits or fac_get_audit by focusing on multi-year trends for a single recipient.

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 implies usage when you need multi-year audit history for a specific recipient via UEI or name. It provides good context but lacks explicit when-not-to-use or reference to sibling alternatives, leaving the agent to infer based on sibling names.

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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