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

Annotations already declare readOnly and idempotent, but the description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts while the rest still returns. This goes beyond the safety profile and describes failure modes and latency.

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 and clearly structured: purpose, usage, output fields, caveats. Each sentence earns its place, though the run-on first sentence could be split. No filler or repetition of 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?

This is a complex composite tool with no output schema, yet the description enumerates the return fields (summary block, per-advisory detail, links, recent versions), explains ecosystem scope, and covers edge cases like timeouts. It gives agents enough information to set expectations and handle partial results.

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%, so the schema fully documents package and version parameters. The description reinforces the scope (npm package) and mentions scoped packages, but does not add meaning beyond the schema's existing descriptions. Baseline 3 is appropriate.

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 verb and resource: a composite 'should I add this npm package' check in ONE call. It names the data sources (deps.dev, bundlephobia) and clearly differentiates from sibling tools like scan_competitor_ai_presence by focusing on package risk/size evaluation.

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?

The description explicitly states when to use it: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides an exclusion and alternative: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', guiding users toward the correct tool for non-NPM packages.

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