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

The description adds behavioral context beyond annotations: explains partial failures, graceful degradation, and that bundlephobia's first measurement can take 5-30 seconds. Annotations already indicate readOnly, idempotent, and non-destructive, and the description aligns with these.

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 with a clear summary, but it is somewhat lengthy. Every sentence adds value, but minor trimming could improve conciseness without losing information.

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 no output schema, the description fully explains return values (summary block with specific fields, per-advisory detail, links, alternative versions) and handles edge cases like partial failures and sources_failed. Complete for a composite tool with multiple data sources.

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 descriptive parameter descriptions. The tool description does not add additional meaning beyond what the schema already provides for package and version. Baseline score of 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 clearly states the tool is a composite check for npm packages using deps.dev and bundlephobia, with a specific verb-resource combination. It distinguishes from sibling tools like validate_claim or deep_research by focusing on dependency scanning.

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 it (questions about safety/popularity/size) and when not to use it (other ecosystems like PyPI, Maven, Cargo, Go, which fall under different tools). It also provides context on partial failures and timing.

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