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Fac

by io.github.pipeworx-io

Fac Federal Awards By Program

fac_federal_awards_by_program
Read-onlyIdempotent

List 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 major program. Answers "who spends the money in this federal grant program and how much does each one run".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cfdaNoAssistance Listing / CFDA number. Accepts "93.224", "93-224", "93 224", "93224", or a bare agency prefix "93" for every program at that agency. Aliases: program_number, assistance_listing, program, cfda_number.
limitNoRows to return, 1-200 (default 25).
stateNo2-letter recipient state code to narrow the ranking, e.g. "TX".
audit_yearNoRestrict to one audit year, e.g. 2023.
major_onlyNoKeep only programs audited as major programs.
min_amountNoOnly rows expending at least this many dollars.
include_recipientsNoResolve each report_id to the recipient name and state (default true; one extra upstream call).
federal_agency_prefixNoTwo-digit agency prefix on its own, e.g. "93" for HHS. Alias: prefix.
federal_award_extensionNoProgram extension on its own, e.g. "224". Alias: extension.

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: -[
-  {
-    "cfda": "93.224",
-    "limit": 3
-  }
-]New value: +[
+  {
+    "audit_year": 2024,
+    "cfda": "93.224",
+    "limit": 10
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds context about resolving recipient names via an extra call (include_recipients parameter), which is helpful. It does not contradict annotations.

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 with a clear action verb and an example. Front-loaded with the primary purpose, no fluff.

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?

Despite having 9 parameters and no output schema, the description explains the output fields (recipient name, state, audit year, major program status) and mentions ranking by dollars. This provides sufficient completeness for a data-listing tool.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description provides an example and summarizes the purpose, which adds context beyond the parameter descriptions. However, it does not detail each parameter individually.

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 clearly states the tool lists organizations by CFDA number, ranked by dollars expended, with key details. It provides examples and answers a specific question, distinguishing it from sibling tools like fac_search_audits or fac_recipient_audit_history.

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?

The description implies usage for finding who spends money in a federal program, but does not explicitly state when to use this tool versus alternatives or provide 'when not to use' guidance. No mention of sibling tool differentiation.

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