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

Annotations declare readOnlyHint=false and idempotentHint=false, which would otherwise look odd for a stats lookup; the description resolves this by disclosing the x402 payment model ($0.001 USDC on Base, charged per successful call), implicitly explaining why repeat calls are not free. It adds real operational context beyond the structured hints, though it doesn't mention rate limits or failure handling.

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?

Three sentences, each carrying distinct payload: what is returned, when to use it, and what it costs. The identifying information is front-loaded and there is no filler.

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 one-parameter, no-output-schema tool, the description covers the return content (day/week/month counts), the data source, and the payment requirement — everything an agent needs to decide to call it and to anticipate being charged.

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 100% and the single 'package' parameter is already documented with examples (react, @scope/name). The description adds no further parameter semantics, so the baseline 3 applies.

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 resource (npm package download counts), the exact scope of data returned (last day, week, month), and the source (official npm registry API). This implicitly separates it from siblings like pypi and package, though it never names an alternative explicitly.

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 to judge how widely a dependency is used' gives a clear motivating context but no when-not conditions and no comparison against the sibling tools (package, package-audit, pypi) that could plausibly answer related questions.

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