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Pypi

by io.github.pipeworx-io

Get Package

get_package
Read-onlyIdempotent

Get metadata for a Python package on PyPI (the Python Package Index). Returns the latest version, summary, author, license, project URLs, required Python version, keywords, classifiers, and the release artifact files that pip install would download. Pass the exact pip package name (e.g. "requests", "numpy").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesExact PyPI package name, e.g. "requests".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name
authorYesPackage author name
licenseYesLicense type
summaryYesShort package description
versionYesLatest version number
keywordsYesPackage keywords
home_pageYesPackage homepage URL
author_emailYesPackage author email
latest_filesYesLatest distribution files
project_urlsYesAdditional project URLs
recent_versionsYesLast 10 available versions
requires_pythonYesPython version requirement

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds value by detailing the returned metadata (version, summary, keywords, etc.), helping the agent understand the output.

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?

Three concise sentences, front-loaded with the core purpose, 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 an output schema present, the description sufficiently covers what the tool returns, and the annotations cover safety. The tool definition is complete for the agent.

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 parameter description is already clear. The description reiterates the example, adding little extra meaning beyond the schema.

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 it retrieves metadata for a Python package on PyPI, listing specific fields (latest version, summary, etc.). It distinguishes from siblings like get_package_version.

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?

It instructs to pass the exact pip package name, providing clarity on input. While no explicit alternatives are given, the purpose is specific enough for an agent to infer when to use.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists among the polymarket tools (e.g., bet_research, polymarket_arbitrage, polymarket_edges) and the ask_pipeworx variants. The detailed descriptions help distinguish them, but an agent might still misselect in those groups.

Naming Consistency4/5

Tool names follow a mostly consistent verb_noun pattern in snake_case. Minor deviations exist, such as 'remember' vs 'recall' and the mixed use of verbs and nouns (e.g., 'ask_pipeworx' vs 'polymarket_arbitrage'), but overall the pattern is predictable.

Tool Count3/5

With 35 tools, the server is on the heavy side. While each tool serves a specific purpose, the sheer number may be overwhelming, and some subsets (like the 7 polymarket tools) could potentially be consolidated. Still, the scope justifies many of them.

Completeness4/5

The server covers a wide range of functionalities: Python package management, company research, prediction markets, monitoring, memory, and data queries. Minor gaps exist (e.g., no direct tool for editing subscriptions), but the surface is generally comprehensive and well-rounded.

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