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

Beyond the annotations, the description discloses important behavioral details: pricing per call, first 25 free, failed calls free, and that repeated calls are charged. It also explicitly confirms the tool only reads or computes and changes nothing, consistent with readOnlyHint=true.

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 front-loaded with what the tool returns, followed by usage, example, and billing details. It is reasonably concise, though the pricing and repeat-charge information is somewhat verbose and partially restates the cost point.

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, one-parameter read-only lookup with no output schema, the description lists the returned fields, gives usage context, an example, and billing/side-effect behavior. Nothing essential for correct invocation appears missing.

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 is fully described in the schema as the PyPI package name. The description adds a concrete example (package=requests), which helps clarify the expected value format beyond the schema's terse description.

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 resource (a Python package on PyPI) and enumerates the returned information: latest version, summary, licence, required Python version, home page, release date and release count. The example and 'Use to check a dependency before installing it' make the tool's purpose unambiguous and distinguish it from sibling data tools.

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?

It gives a clear usage context: check a dependency before installing it, with an example call. It does not mention alternatives or when not to use it, but there is no obvious overlapping sibling for PyPI package metadata.

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