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

The description goes beyond annotations (readOnlyHint, etc.) by disclosing behavioral traits: it fans out to two services, returns specific fields, notes that bundlephobia's first measurement can take 5-30s, and mentions graceful degradation with partial failures. No contradiction with 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 appropriately sized and well-structured: it front-loads the main purpose, then details return values, ecosystem notes, and failure behavior. Every sentence provides necessary information without redundancy.

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?

Despite lacking an output schema, the description fully lists return fields (summary block, advisories, links, alternative versions) and covers edge cases like partial failures and timeouts. It provides sufficient information for the agent to understand the tool's behavior and return format.

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%, so the description adds value beyond the schema by clarifying that version defaults to latest and scoped packages are accepted. This provides helpful context not present in the schema alone.

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 adding an npm package, with specific verbs ('fans out', 'returns') and resources (deps.dev, bundlephobia). It distinguishes itself by focusing on npm ecosystem and combining multiple checks in one call, differentiating from other scanning tools.

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?

Explicit guidance is given on when to use: 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'.' It also provides a when-not-to-use example: 'PyPI / Maven / Cargo / Go fall under deps.dev:version directly,' clarifying alternatives for other ecosystems.

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