python-docs-mcp-server

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lookup_package_docs

Read-onlyIdempotent

Retrieve official package documentation, homepage, and source URLs by querying PyPI metadata. Get reliable project links directly from the Python Package Index.

Instructions

Look up package-declared docs/homepage/source URLs via official PyPI metadata.

This is not generic web search: it only queries PyPI's JSON API and returns official PyPI metadata plus package-declared project URLs.

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": {
      "package": "<package>"
    },
    "name": "lookup_package_docs"
  }
}

Input Schema

Name	Required	Description	Default
`package`	Yes	PyPI package/project name

Output Schema

Name	Required	Description	Default
`note`	No	Controlled-scope note, for example skipped labels or not-found details
`package`	Yes	Canonical package name returned by PyPI when available
`sources`	No	Package-declared PyPI, documentation, homepage, and source URLs
`summary`	No	Package summary from PyPI metadata
`version`	Yes	Latest version reported by PyPI metadata
`trust_boundary`	No	Indicates results are limited to PyPI/project-declared metadata	pypi-declared-metadata
`metadata_source`	Yes	Official PyPI JSON API URL used for lookup

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 13, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds that it only queries PyPI's JSON API and returns official metadata and project URLs, providing behavioral context beyond annotations. 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 short paragraphs, front-loaded with purpose, followed by clarifying scope. Every sentence adds value with no wasted words.

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?

With one simple parameter, high schema coverage, output schema exists, and annotations covering safety, the description provides all necessary context: what it does, its data source, and its limitations. Complete for the tool's complexity.

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% and the schema already describes the 'package' parameter as 'PyPI package/project name'. The description does not add new meaning beyond what the schema provides, so baseline 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?

Description clearly states it looks up package-declared docs/homepage/source URLs via official PyPI metadata, using specific verb 'look up' and resource 'package-declared URLs'. It distinguishes from generic web search and from siblings like search_docs by emphasizing it only queries PyPI's JSON API.

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?

Explicitly states it is not generic web search and only queries PyPI's JSON API, providing clear context on when not to use. While it does not explicitly name siblings as alternatives, the contrast with general search is sufficient.

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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