lookup_pypi_package

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

With no annotations provided, the description fully carries the transparency burden. It details the behavioral signals returned (age, downloads, release consistency, etc.) and includes relevant context about supply chain attacks, giving comprehensive insight into tool behavior.

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 at 4-5 sentences, front-loaded with the main action, and efficiently organized with signals, context, and use cases. Every sentence adds value without unnecessary fluff.

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 simple 1-parameter tool without an output schema, the description adequately explains the return signals and use cases, making it sufficiently complete for an agent to understand what the tool provides.

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 sole parameter 'package' has full schema description coverage (100%) with examples and case-insensitivity noted. The tool description adds context about why the parameter is important (vetting dependencies, supply chain risk), providing value beyond the schema.

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 gets a behavioral commitment profile for any PyPI package, specifying the resource (PyPI) and action. It distinguishes from siblings like lookup_npm_package by focusing on Python packages.

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 use cases (vetting dependencies, abandonware, supply chain risk) and examples, offering clear context for when to use the tool. However, it does not explicitly mention when not to use it or compare to alternatives like audit_dependencies.

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