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court_docket

Look up a federal court docket by ID via CourtListener RECAP archive. Parties, filings, dates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
docket_idYesDocket ID

Schema Changelog

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

  1. First observed

TDQS

B3.1/5.0
Behavior1/5

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

With no annotations present, the description carries full burden of behavioral disclosure. It only states the basic lookup action and omits critical traits: read-only nature, API rate limits, authentication requirements, data freshness, or whether it supports partial IDs. The agent cannot infer safety or limitations from this description alone.

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 two sentences with zero wasted words. It front-loads the action and resource, then lists what the tool returns. Every word adds value.

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?

For a single-parameter tool with no output schema, the description covers the core purpose and expected outputs. However, it lacks detail on ID format, return structure (e.g., fields beyond parties/filings/dates), and limitations (e.g., only federal courts, RECAP coverage). It is adequate but incomplete for optimal use.

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 coverage is 100%, so the baseline is 3. The description adds 'federal court docket' and 'via RECAP archive' but does not clarify the expected format of docket_id (e.g., case number format, court abbreviation). The parameter's schema description is generic ('Docket ID'), and the tool description adds marginal context without specifying syntax or validation.

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's purpose: 'Look up a federal court docket by ID via CourtListener RECAP archive.' It specifies the resource (federal court docket), the action (look up), the data source (RECAP archive), and the return type (parties, filings, dates). It distinguishes itself from siblings like court_opinions and judges_search by focusing on docket records.

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?

The description provides no guidance on when to use this tool vs alternatives. It implies use when you have a docket ID, but does not mention prerequisites, exclusions, or when to choose another tool. For example, no mention of whether state court dockets are excluded or what to do if the ID is not found.

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

C2.2/5.0
Disambiguation3/5

Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.

Tool Count1/5

95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.

Completeness2/5

Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.

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