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

Discloses behavioral details beyond annotations: bundlephobia's first measurement can take 5-30 seconds, sources_failed will list timeouts, partial failures degrade gracefully. Annotations already indicate read-only, open world, idempotent, and non-destructive, so the description adds critical real-world behavior.

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?

Description is front-loaded with the core purpose, then details sources, usage, ecosystem scope, and error handling. Every sentence is informative and concise, with no filler. It is structured logically and appropriately sized.

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, but the description enumerates the return fields (summary block, per-advisory detail, links, alternative versions). It covers input, behavior, error handling, and ecosystem limitations. The tool is complex with multiple sources, and the description provides a complete mental model.

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 coverage is 100% (both parameters have descriptions). The description adds that scoped packages like '@types/node' are accepted for the 'package' parameter, which is not in the schema's description. For 'version', it reinforces the default to latest. It adds modest value beyond the schema.

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?

Describes a composite check for npm packages combining deps.dev and bundlephobia data, clearly stating the purpose: a one-call 'should I add this npm package' check. Distinguishes itself by specifying the scope (NPM ecosystem) and the exact questions it answers (safe, popular, small, cost of adding). It is distinct from sibling tools like scan_competitor_ai_presence.

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?

Explicitly states when to use: 'whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me''. Also gives clear exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.' Provides guidance on partial failures and timing (bundlephobia 5-30s first measurement).

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