dev_pypi

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

Discloses cost ($0.003 USDC per call), caching behavior (up to 1 hour), normalization (PEP 503), and full list of returned data fields. Since no annotations provided, description fully covers behavioral traits.

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?

Description is somewhat long but every sentence adds value: cost, purpose, use cases, data returned, caching. Front-loaded with cost and main purpose. Minor redundancy possible but still efficient.

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 single-parameter tool with no output schema, description covers cost, caching, normalization, use cases, and return fields. Fully informs an AI agent about what the tool does and how to use it.

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?

Schema coverage is 100% but description for packageName repeats schema description ('PyPI package name (PEP 503 normalized, e.g. 'requests', 'scikit-learn').') without adding further meaning beyond what schema already provides. Baseline 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?

Description explicitly states 'Check a PyPI package's version freshness, yank status, and popularity.' with specific verb 'Check' and resource 'PyPI package'. Clearly distinguishes from sibling dev_npm by specifying PyPI.

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?

Explicitly lists use cases: 'auditing requirements.txt and pyproject.toml, supply-chain security checks, and version freshness agents.' Also mentions caching. No explicit when-not-to-use, but context is clear enough.

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