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

Beyond the readOnly/idempotent annotations, the description discloses important runtime behavior: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. This context helps agents set expectations and handle errors appropriately, exceeding what annotations provide.

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 a single dense paragraph but every sentence adds value: purpose, use case, return fields, ecosystem scope, and failure behavior. It is front-loaded with the core check and avoids redundancy with the schema or annotations.

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 complexity (composite fan-out, multiple return fields, external service dependencies), the description covers all key aspects: data sources, exact return fields, ecosystem boundaries, latency, and degradation behavior. Without an output schema, this description fully compensates.

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?

The schema already describes both parameters completely (npm package name, scoped package acceptance, version default). The description adds no new semantic information for the parameters—only contextual behavior like latency. With 100% schema coverage, this is the baseline score.

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 opens with a clear composite purpose: 'should I add this npm package to my project' check in ONE call, and explicitly names the external sources (deps.dev and bundlephobia) and the data categories. This distinctly separates it from sibling tools like scan_competitor_ai_presence or ai_visibility_check, which target different domains.

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?

It gives explicit trigger phrases: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also states an exclusion for non-NPM ecosystems and directs users to deps.dev:version directly, providing a clear alternative.

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