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cyntrica

Gov Data MCP

by cyntrica

congress_house_votes

Read-only

Fetch U.S. House of Representatives roll call votes with member-level party breakdowns. Covers 1990 to present. Filter by congress, session, year, or vote number.

Instructions

Get House of Representatives roll call vote results with member-level party breakdown. Primary source: Congress.gov API (118th-119th Congress); falls back to clerk.house.gov XML for older congresses. Coverage: 1990 to present. Use year param for historical votes. Cross-reference with: congress_senate_votes (same bill's Senate vote), FEC (congress_member donors via fec_candidate_financials), lobbying_search (who lobbied on the bill), FRED (economic impact 1-3 years after passage). For Senate votes, use congress_senate_votes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoCalendar year (e.g. 2024). Overrides congress+session if provided.
limitNoMax results when listing votes (default: 20)
sessionNoSession (1 or 2). Default: current session
congressNoCongress number (default: current). Used with session to determine year.
vote_numberNoSpecific roll call vote number. Omit to list recent votes.
Behavior4/5

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

The description discloses the data source fallback (Congress.gov to clerk.house.gov XML for older congresses) and coverage range (1990 to present), which are behavioral traits beyond the read-only annotation. It does not contradict the annotations, and the read-only hint aligns with 'Get'.

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?

The description is concise, with each sentence serving a distinct purpose: purpose, sources, and usage guidance. It avoids filler and front-loads the core function.

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 read-only vote retrieval tool with a rich schema and no output schema, the description provides a solid overview of sources, coverage, and related data, though it doesn't elaborate on return structure or pagination. The cross-reference guidance adds valuable context for an agent.

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 schema already provides 100% descriptive coverage for all five parameters, so the description adds minimal extra parameter detail beyond implying the year parameter for historical votes. Since the schema handles the heavy lifting, a baseline score of 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 clearly states the tool retrieves House roll call votes with member-level party breakdown, and explicitly distinguishes it from the Senate counterpart, naming congress_senate_votes as the alternative. It also specifies the primary source and coverage, leaving no ambiguity about its function.

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

Usage Guidelines5/5

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

It explicitly instructs to use congress_senate_votes for Senate votes, provides a cross-reference list for complementary data, and advises using the year parameter for historical queries. This gives clear when-to-use and when-not-to-use guidance.

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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