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

Goes well beyond the readOnly/openWorld/idempotent annotations by disclosing the exact resolution algorithm (as-is, PEP 503 normalization, [-_.] variants), the redaction of author emails, and the paid-call mechanics including price, currency, and how to obtain the PaymentRequired object. This is unusually rich behavioral disclosure.

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?

Front-loaded with the usage condition and packs algorithm, return fields, redaction, and payment into two dense sentences with no filler. The payment clause is long but operationally necessary, so it earns its place.

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?

With no output schema, the description compensates by enumerating returned fields (summary, version, requires_python, license), and it covers the non-obvious auth/payment path. Nothing needed to call it correctly is 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by explaining how the 'q' value is interpreted (normalization and variant expansion) and that 'limit' caps returned matching projects — behavior the schema text alone does not convey.

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?

States a specific verb+resource (resolve a PyPI project name to live projects) and immediately scopes it against the sibling category by declaring 'This is name resolution, not full-text discovery,' which separates it from npm_search-style 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?

'Use when you have a likely PyPI project name (or a few tokens)' gives clear triggering context, and 'prefer exact package names when known' adds a preference rule. However, it never names the concrete alternative tools (pypi_project, npm_search) an agent should fall back to.

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