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by ai.bitroad
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Catalog describe category

catalog_describe_category
Read-onlyIdempotent

Fetch one category by slug, including the spec JSON Schema for leaf categories. Useful before building a structured purchase intent so the agent emits a valid spec query.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "catalog_describe_category"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesSlug of the category to describe, e.g. "electronics/phones/smartphones".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description discloses that the schema is included only for leaf categories, which is a behavioral nuance beyond the read-only, idempotent annotations. This adds helpful detail about what the response will contain.

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, concise and directly to the point, with no unnecessary words or redundancy. It efficiently conveys purpose and usage context.

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

Completeness4/5

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

Given the absence of an output schema, the description sufficiently covers what the tool does and when to use it. It could mention error cases or non-leaf behavior, but the provided context is adequate for an agent to invoke it correctly.

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 only parameter 'slug' is fully described in the input schema with a minLength and example. The description does not add additional meaning beyond the schema, so a baseline score of 3 is appropriate given 100% schema coverage.

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 verb 'Fetch' and the resource 'one category by slug', with the additional detail of including the spec JSON Schema for leaf categories. This distinguishes it from sibling tools like catalog_list_categories and catalog_get_product.

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

Usage Guidelines4/5

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

The description explicitly mentions when it is useful ('before building a structured purchase intent') and why ('so the agent emits a valid spec query'), providing clear contextual guidance. It does not explicitly state when not to use it, but the use case is well-defined.

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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