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

Beyond the readOnly/idempotent hints, the description discloses important behavioral traits: partial failures degrade gracefully, the first bundlephobia measurement can take 5-30s, and sources_failed will list timeouts while the rest of the results still return. This gives the agent essential expectations about latency and partial results.

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 dense but front-loaded with the core purpose, followed by usage guidance, return structure, ecosystem scope, and failure behavior. Every sentence earns its place; no filler or repetition.

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 thoroughly explains the return format (summary block fields, per-advisory details, links, alternative versions). It also covers ecosystem limitations and timeout behavior, making the tool's behavior fully predictable for an agent.

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 provides 100% coverage of both parameters (package and version) with clear descriptions, so the description doesn't need to repeat them. The description adds no extra parameter-specific semantics beyond what the schema offers, which is acceptable given the high schema coverage.

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 clearly states it is a composite check for 'should I add this npm package to my project' and specifies the exact data sources (deps.dev and bundlephobia) and what they measure (license, advisories, bundle size, etc.). This distinguishes it from sibling tools like validate_claim or scan_competitor_ai_presence, which serve different functions.

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 says 'Use whenever an agent asks...' and gives concrete examples like 'is X safe / popular / small' or 'what does adding lodash cost me'. It also provides an alternative for non-NPM ecosystems (deps.dev directly), making the when-to-use and when-not-to-use guidance clear.

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