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Crs Reports

by io.github.pipeworx-io
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Crs Topics

crs_topics
Read-onlyIdempotent

The Congressional Research Service's own subject taxonomy, with how many reports carry each term — the legal filter values for crs_search and crs_recent. Also lists CRS analysts by output, and the product families. Call this before filtering by topic: CRS writes 'U.S. Trade Policy', and a guessed 'Trade' matches nothing.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "facet": "topics",
      "limit": 20
    },
    "name": "crs_topics"
  }
}

Input Schema

NameRequiredDescriptionDefault
facetNoWhich vocabulary to list. Default topics.
limitNoValues to return, 1–500. Default 100.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds the key behavioral fact that returned values are the 'legal filter values' for crs_search/crs_recent, which is beyond the annotations and useful for the agent. No contradiction.

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?

Two sentences, front-loaded with the core purpose, then a critical usage warning. No filler; every phrase earns its place.

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?

Complete for a read-only vocabulary listing tool. Safety is covered by annotations, params are fully documented, and the description explains the return nature (lists) and why to call it, making it sufficient for correct invocation.

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

Parameters4/5

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

Schema coverage is 100% with clear descriptions and enums. The description adds semantic meaning to the facet parameter by mapping topics to the taxonomy, authors to analysts, and product_class to product families, enriching the enum values beyond their labels.

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?

States a specific verb ('lists') and resource (CRS taxonomy with counts), and explicitly ties the values to crs_search and crs_recent, distinguishing it from those siblings. The additional mention of analysts and product families clarifies its full scope.

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?

Explicitly instructs to call before filtering by topic and gives a concrete example of why ('U.S. Trade Policy' vs 'Trade' matches nothing). This provides clear when-to-use guidance and implies it is a prerequisite for topic filtering.

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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