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

The description discloses behavioral details beyond annotations: partial failure mode (bundlephobia timeout, sources_failed list), that it returns both summary and detail blocks, and that it fans out to multiple sources. This adds significant context beyond the readOnlyHint and idempotentHint 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 well-structured and front-loaded, but slightly verbose with examples and details. Every sentence adds value, but it could be trimmed slightly without loss of clarity.

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 and lack of output schema, the description thoroughly explains the return format (summary block, per-advisory detail, links, alternative versions) and error handling (partial failures, timeout). It is complete for an agent to understand and invoke the tool correctly.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by noting that scoped packages are accepted for the 'package' parameter and that 'version' defaults to the latest, providing clarity beyond the schema descriptions.

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 evaluating npm packages across deps.dev and bundlephobia. It distinguishes from sibling tools (e.g., deep_research, bet_research) as the only tool specifically for npm package 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 says when to use: for questions like 'is X safe/popular/small' or 'what does adding lodash cost me'. It also states the limitation to npm ecosystem v1 and directs users to deps.dev:version for other ecosystems, providing clear when-not-to-use guidance.

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