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

While annotations already mark readOnlyHint=true, openWorldHint=true, and idempotentHint=true, the description adds genuine behavioral context: it discloses latency (bundlephobia's first measurement can take 5-30s), graceful degradation via 'sources_failed', and the fact that partial results still return. This goes beyond the structured annotations.

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 information-dense but well organized: purpose, use-cases, return fields, ecosystem scope, and failure handling are each covered in separate clauses. It is longer than minimal but every phrase contributes meaningful context, and the lead sentence immediately states the core value proposition.

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 (multiple external API integrations) and the lack of an output schema, the description compensates by enumerating return fields (summary block, per-advisory detail, links, alternative versions) and explaining source-specific behavior (bundlephobia timeout handling, NPM-only v1). It gives the agent enough to anticipate output structure and edge cases.

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 input schema fully describes both parameters (package name with scoped package examples and version with default behavior), so schema coverage is 100%. The description adds no additional parameter-level meaning beyond what the schema already provides; it only restates the default version behavior. Baseline 3 is appropriate per the rubric.

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 specific composite check ('should I add this npm package to my project') and names both upstream services (deps.dev and bundlephobia) with concrete data points (license, advisories, version history, bundle size, dependency count, ESM/tree-shake support). This clearly distinguishes it from sibling research tools and immediately conveys the tool's scope.

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 explicitly states when to use the tool ('Use whenever an agent asks...') and provides negative guidance: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' This tells the agent both appropriate use cases and a fallback alternative, which is exactly what usage guidelines should do.

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