lookup_pypi_package

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

With no annotations, the description carries the full burden. It discloses behavioral signals: 'package age, download volume and trend, release consistency, publisher/owner count, and linked GitHub activity.' It also mentions a real-world supply chain attack to illustrate relevance. While it lacks details on output format or performance, the description is sufficiently transparent about what the tool returns.

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 concise, with three clear sections: purpose, detailed signals, and usage scenarios. It uses bold text for key phrases and provides specific examples. Every sentence adds value without redundancy.

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 lookup tool with a single parameter and no output schema, the description is fairly complete. It explains what the tool does, what signals are returned, and when to use it. Minor gaps include lack of output format details, but these are secondary given the tool's simplicity.

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 covers the sole parameter 'package' with a description of 'PyPI package name' and examples. The description does not add new information beyond the schema; it merely repeats examples. With 100% schema coverage, a 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 purpose: 'Get a behavioral commitment profile for any PyPI (Python) package.' It specifies the resource (PyPI packages) and the action (getting a profile). Sibling tools like lookup_npm_package and lookup_go_module differentiate by ecosystem, so the purpose is distinct.

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 provides explicit usage contexts: 'vetting Python dependencies, identifying abandonware, supply chain risk due diligence.' It also lists examples of packages. However, it does not explicitly state when not to use the tool or mention alternatives, which would justify a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.