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lzinga

US Government Open Data MCP

congress_bill_full_profile

Read-only

Get a complete bill profile in one call, combining details, sponsors, actions, summaries, committees, subjects, text versions, related bills, and titles from multiple endpoints.

Instructions

Get a COMPLETE bill profile in ONE call — combines bill details, all cosponsors (with party breakdown), full action timeline, CRS summaries, committees, legislative subjects, text versions, related bills, and all titles. Fetches 8 endpoints in parallel. Use this instead of calling congress_bill_details + congress_bill_actions + congress_bill_summaries + congress_bill_committees + congress_bill_subjects + congress_bill_text + congress_bill_related + congress_bill_titles individually.

Ideal for: Complete legislative analysis, bill research, accountability investigations, or getting everything needed to cross-reference with FEC (who funded the sponsors), lobbying_search (who lobbied), and FRED (economic impact after passage).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
congressYesCongress number (e.g., 119, 118, 117)
bill_typeYesBill type
bill_numberYesBill number (e.g., 1, 25, 3076)
Behavior5/5

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

The description reveals a key behavioral trait: 'Fetches 8 endpoints in parallel', which adds value beyond the readOnlyHint annotation. It fully discloses the composite nature and scope of data returned, with no contradiction to 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?

The description is concise, using two sentences to pack essential information. The first sentence lists all combined endpoints, and the second provides ideal use cases. Every sentence adds value, with no redundancy.

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

Completeness5/5

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

Despite lacking an output schema, the description enumerates all the components returned (bill details, cosponsors, actions, etc.) and outlines practical use cases. This provides sufficient context for an agent to understand the tool's scope and output.

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 covers all three parameters (congress, bill_type, bill_number) with 100% description coverage, including examples. The description does not add additional semantics beyond what the schema already provides, so 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 explicitly states the tool gets a 'COMPLETE bill profile in ONE call' and lists the specific endpoints combined (bill details, cosponsors, actions, summaries, etc.), clearly distinguishing it from sibling tools like congress_bill_details by saying 'Use this instead of calling congress_bill_details + ...'.

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 provides explicit guidance: 'Use this instead of calling' individual tools, and identifies ideal use cases: 'Complete legislative analysis, bill research, accountability investigations'. It also suggests cross-referencing with related tools (FEC, lobbying_search, FRED), offering clear when-to-use and when-not-to-use context.

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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