Retrieve Python package information from PyPI (Python Package Index). Returns current version, download counts, dependencies, release history, package homepage, and PyPI page URL. Use for Python library evaluation, dependency analysis, or checking package quality metrics.
| Name | Required | Description | Default |
|---|---|---|---|
| package_name | Yes | PyPI package name as listed in registry (e.g. 'numpy', 'django', 'flask', 'pandas') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is known. The description adds value by specifying the exact data returned (current version, download counts, dependencies, release history, homepage, PyPI URL), which is useful behavioral context beyond the annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the action and resource. Every sentence adds value—first the purpose, then the return values and use cases. No redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter read-only tool with strong annotations and clear return field enumeration, the description is complete. It covers what the tool does, what it returns, and when to use it. The absence of an output schema is mitigated by the explicit list of returned data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a clear description of 'package_name' (e.g., 'numpy', 'django'). The description does not add much beyond the schema, but the schema itself is sufficiently descriptive, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb 'Retrieve' and a specific resource 'Python package information from PyPI'. It lists concrete return fields (version, download counts, dependencies, etc.), which distinguishes it from sibling tools like get_npm_package and get_github_repo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states use cases: 'Use for Python library evaluation, dependency analysis, or checking package quality metrics.' This gives clear context for when to use the tool, but it does not mention when not to use it or name alternative tools for other package registries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: get for specific GitHub repos and npm/PyPI packages, search for GitHub repos, arXiv papers, Google Scholar papers, and Stack Overflow Q&A. No two tools overlap in purpose.
All tool names follow a consistent verb_source pattern using snake_case (get_github_repo, get_npm_package, search_arxiv, etc.). This makes the set predictable and easy to navigate.
With 7 tools, the server covers a focused domain—developer research and resource evaluation—without bloat. Each tool contributes a distinct function, and the count is well-balanced for the scope.
The tool surface covers fetching metadata for known repos/packages and searching multiple external platforms. A minor gap is the lack of direct search for npm or PyPI packages, but GitHub search partially fills this need. Overall, lifecycle coverage is appropriate for a read-only research assistant.