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legislation

Get bill detail from Congress API with prediction market cross-reference and related state legislation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
billIdYesBill ID, e.g. "119-hr-22"

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, leaving the description to carry the burden. It discloses the data source and the additional data returned, which gives some behavioral context. Yet it does not mention potential external API call behavior, error handling, rate limits, or return format. The verb 'Get' implies a read-only operation, but beyond that, the description is thin on behavioral details.

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 a single, efficient sentence that leads with the core action 'Get bill detail' and then appends relevant modifiers. Every word adds value, with no redundancy or fluff. It is appropriately sized for the tool's simplicity.

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

Completeness3/5

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

With one well-documented parameter and no output schema, the description provides the essential purpose and invocation details. It mentions extra data fields (prediction market cross-reference, state legislation), but it does not describe return structure, potential failures, or when to choose this tool over similar siblings. Given no annotations, there are notable gaps in context.

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?

Schema description coverage for the single parameter 'billId' is 100%, including an example ('119-hr-22'). The tool description itself does not add parameter semantics, but the schema already fully documents the parameter, so the baseline of 3 applies.

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

Purpose4/5

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

The description uses the verb 'Get' with resource 'bill detail', clearly stating the tool's core function. It adds specificity by naming the source 'Congress API' and mentioning extra outputs ('prediction market cross-reference' and 'related state legislation'). However, it does not explicitly distinguish itself from the sibling tool 'get_legislation', which likely serves a similar purpose, so it stops short of a 5.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives like 'get_legislation' or 'list_legislation'. The description implies use for bill details with extra context, but it does not provide any when/when-not directions, exclusions, or alternative tool references.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple market query tools (scan_markets, screen_markets, get_market_detail, get_market_diff, get_market_history, inspect_ticker) and legislative tools (legislation, get_legislation, list_legislation, query_gov). Aliases like get_heartbeat_config/get_heartbeat_status and explore_public/explore_theses add further confusion. An agent would struggle to select the correct tool without deeply reading each description.

Naming Consistency3/5

Most tools follow a verb_noun pattern (get_, list_, create_, update_), but there are notable deviations: 'legislation' lacks the 'get_' prefix, 'stt' and 'tts' are acronyms, 'monitor_the_situation' is a full phrase, and 'x_account/x_news/x_volume' use a non-standard prefix. The overall style is readable, but the mixed conventions reduce predictability.

Tool Count1/5

108 tools is extreme for any server, even one covering prediction markets, trading, portfolio management, forum, skills, and speech. The massive surface area overwhelms agents and makes the server feel more like a platform than a coherent toolkit. This many tools inevitably leads to redundancy and maintenance burden.

Completeness4/5

The server covers an impressively broad domain: market data, thesis management, intents, strategies, positions, portfolio, forum, skills, legislative and economic queries, and audio/visual processing. Minor gaps exist (e.g., no delete for skills/theses, no update for some portfolio items) but core workflows are well-supported. Overall lifecycle coverage for most entities is strong.

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