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fec_committee_search

Read-onlyIdempotent

Search FEC-registered political committees (campaign committees, PACs, party committees, Super PACs) by name, state, or committee type. Returns committee IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoTwo-year election cycle (even year, e.g. 2024). Optional.
limitNoMaximum committees to return (default 20, max 100).
queryNoCommittee name fragment. Optional.
stateNoTwo-letter state code to filter by. Optional.
committee_typeNoCommittee type code: 'P' (President), 'S' (Senate), 'H' (House), 'N'/'Q' (PAC), 'O' (Super PAC), 'X'/'Y' (party). Optional.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond that: it clarifies that the search covers multiple committee categories and that the result is committee IDs. This is adequate for a safe lookup tool, though pagination and no-results behavior are not mentioned.

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?

A single sentence covers the resource, scope, filter options, and return value with no filler. The most important information is front-loaded, and every phrase earns its place.

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?

For a zero-required-parameter search tool with a fully documented schema and rich annotations, the description is largely complete. It explicitly states the return value ('Returns committee IDs'), compensating for the lack of an output schema, though the exact result shape and edge-case behavior are left unspecified.

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?

The input schema has 100% description coverage for all five parameters, so the description does not need to restate parameter details. However, it does add the high-level search dimensions ('by name, state, or committee type') that map to query, state, and committee_type, providing mild complementary value.

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 uses a specific verb ('Search'), a clear resource ('FEC-registered political committees'), enumerates the covered committee categories, and states the searchable dimensions (name, state, committee type). It also distinguishes itself from sibling tools by scoping to committees rather than candidates.

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 this tool is for finding FEC committees by name, state, or type, but it does not explicitly name alternatives or state when not to use it. An agent must infer routing from the tool name and sibling names like fec_candidate_search rather than being told directly.

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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