US Government Open Data MCP

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's process ('Scans the bill's action timeline for recorded vote references, then fetches each House and Senate vote with member-level results and party tallies') and output ('Returns all House and Senate roll-call votes associated with the bill, with full party breakdowns'). However, it lacks details on error handling, rate limits, authentication needs, or whether this is a read-only operation. For a tool with no annotations, this provides basic behavioral context but misses important operational details.

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

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

The description is well-structured and appropriately sized. It front-loads the core purpose in the first sentence, then provides additional context about the tool's role in investigations. The bulleted workflow section efficiently communicates usage guidelines. While slightly verbose, every sentence adds value and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (bill-to-vote mapping with party breakdowns), no annotations, and no output schema, the description does a good job of explaining what the tool does and how it fits into a larger workflow. It clearly states the input requirements (bill identification) and output (all roll-call votes with party tallies). However, it could be more complete by explicitly mentioning that it returns both House and Senate votes (though implied) and providing more detail on the output structure.

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

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

The schema description coverage is 100%, with all three parameters (congress, bill_type, bill_number) clearly documented in the schema. The description does not add any parameter-specific information beyond what the schema provides. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.

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

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

The description explicitly states the tool's purpose: 'Find ALL roll-call votes on a specific bill and fetch the party-line breakdowns.' It specifies the verb ('find' and 'fetch'), resource ('roll-call votes on a specific bill'), and scope ('ALL' with 'party-line breakdowns'). It clearly distinguishes itself from sibling tools by focusing on bill-to-vote mapping, unlike other congress tools that handle amendments, committees, or member details.

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

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

The description provides explicit guidance on when to use this tool versus alternatives. It positions this as 'the KEY tool for 'follow the money' investigations' and outlines a workflow: 'Bill → Votes (this tool), Votes → Who voted how (party tallies returned here), Who voted → Who funded them (fec_candidate_financials / fec_committee_disbursements), Who lobbied → lobbying_search.' This clearly indicates this tool is for connecting bills to votes, with other tools handling subsequent steps.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.