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

Goes well beyond the annotations: it discloses pricing ($0.003/call via x402 USDC or card key, first 25 free, failed calls free) and explicitly reconciles the counterintuitive readOnlyHint=false by explaining the call is charged even though it 'reads or computes only'. That is exactly the kind of context annotations cannot carry.

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 purpose, then usage, then pricing/auth caveats — no sentence is wasted. The 'Example: package=hono' fragment is mildly redundant with the schema's own example, keeping it just under top marks.

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?

No output schema exists, and the description does convey the returned shape (counts for day/week/month) plus auth and billing prerequisites. It omits any mention of error/rate-limit behavior, but for a single-parameter read tool this is close to complete.

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 parameter is already documented with a format example in the schema ('react or @scope/name'). The description's 'Example: package=hono' adds a concrete case but no new syntax or constraint, 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 verb+resource (download counts of an npm package), the exact time granularities returned (last day, week, month), and the data source (official npm registry API). The 'npm' qualifier cleanly separates it from the sibling pypi tool without needing to name it.

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 context for when to reach for it, and the npm scope implicitly routes Python-package lookups elsewhere. It stops short of naming the pypi sibling as the explicit alternative, so it is clear-but-not-exhaustive.

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