fetch_pypi_docs

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it extracts relevant sections, converts reStructuredText to Markdown, prioritizes useful content, and falls back to GitHub README. However, it lacks details on error handling, rate limits, or authentication needs, which are common for API tools, leaving some behavioral aspects unclear.

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 well-structured with clear sections (purpose, usage guidelines, parameters, returns, example) and front-loaded key information. Every sentence adds value without redundancy, making it efficient for an agent to parse while maintaining completeness.

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?

Given the tool's moderate complexity (3 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, usage, parameters, and return format. However, without an output schema, it could benefit from more detail on the JSON structure (e.g., specific fields like 'content', 'size', 'truncation_status') to fully guide the agent.

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 description coverage is 0%, so the description must compensate. It adds meaningful semantics for all three parameters: 'package' is explained with examples, 'max_bytes' specifies a default and purpose, and 'ignore_verification' clarifies its conditional use. This goes beyond the bare schema, though it could provide more detail on parameter interactions or constraints.

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 with specific verbs ('fetch', 'extracts', 'converts') and resources ('Python package documentation from PyPI README/description'). It explicitly distinguishes itself from alternatives like curl, WebFetch, and external documentation sites, making its scope unambiguous.

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 guidance with dedicated sections: 'USE THIS WHEN' lists specific use cases (installation instructions, usage examples, etc.), 'BEST FOR' clarifies its primary function, and 'NOT SUITABLE FOR' explicitly excludes external documentation sites while suggesting an alternative (docs_url from pypi_metadata + WebFetch). This comprehensive guidance helps the agent choose correctly among siblings.

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