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

Despite comprehensive annotations (readOnlyHint, etc.), the description adds significant behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s and may time out, and the sources_failed field will list it. This goes beyond the annotations' binary hints.

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 densely packed but front-loaded with purpose. Each sentence adds distinct information (purpose, usage, returns, limitations, ecosystem scope, error behavior). While lengthy, it is efficient and well-organized, earning a high score.

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 check, multiple sources, partial failures), the description is remarkably complete. It covers inputs, outputs (summary block, per-advisory, links, alternatives), error handling, and ecosystem restrictions. No output schema exists, but the description lists return fields explicitly.

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% with descriptions for both parameters. The description adds value by clarifying that scoped packages (like @types/node) are accepted and that version defaults to latest when omitted. This extra context justifies a score above baseline.

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 the tool's purpose: a composite check for npm packages (license, advisories, version history, bundle size, ESM/tree-shake). It uses specific verbs ('scan', 'fans out across') and resource identifiers, distinguishing it from sibling tools that focus on 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?

The description explicitly tells when to use the tool: 'whenever an agent asks is X safe / popular / small' or 'what does adding lodash cost me'. It also provides an exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', guiding the agent to alternatives.

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