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 indicate readOnlyHint=true and openWorldHint=true, and the description aligns by describing a safe read operation. It adds detail about returned fields (version, downloads, etc.) without contradicting annotations. However, it does not mention potential rate limits or external API constraints.
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 extremely concise: two sentences that front-load the core purpose and return details. There is no extraneous information; every sentence adds value.
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?
Given the tool's simplicity (one parameter, no output schema but listed returns), the description fully covers what the tool does, how it works, and what it returns. No gaps remain for this use case.
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 single parameter 'package_name' is already well-described in the schema (100% coverage). The description adds no additional parameter-level information, so it meets the baseline of 3 without further enhancement.
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 retrieves Python package information from PyPI, listing specific data returned like version and downloads. It distinguishes itself from sibling tools that target different registries (npm, GitHub).
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 mentions use cases: 'Python library evaluation, dependency analysis, or checking package quality metrics.' Though it doesn't explicitly exclude scenarios, the use cases are well-defined and contextually differentiate from siblings.
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 data source and operation: GitHub repos, npm packages, PyPI packages, arXiv papers, GitHub search, Google Scholar, and Stack Overflow. Even the two GitHub tools differ in purpose (get specifics vs search). No overlap.
All tool names follow a consistent verb_noun pattern using snake_case: get_* and search_*. The verbs are precise and the nouns clearly indicate the target resource.
With 7 tools, the set is well-scoped for a developer toolkit covering package registries, code search, academic resources, and Q&A. Each tool serves a distinct purpose without bloat.
The set covers major developer resources (GitHub, npm, PyPI, arXiv, Google Scholar, Stack Overflow). Missing are tools for other registries (e.g., Maven, Docker Hub) and package search for npm/PyPI (only get by name), but core workflows are well represented.