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

Annotations already establish read-only/idempotent behavior, and the description adds valuable behavioral context: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, sources_failed will list timeouts, and the return structure is fully outlined. This goes beyond the annotations to set accurate agent expectations.

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 dense but every sentence serves a purpose: it explains the aggregation, the return shape, ecosystem scope, and failure behavior. It is appropriately front-loaded with the core 'composite check in ONE call' concept and avoids fluff.

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 having no output schema, the description enumerates the exact return fields, per-advisory details, links, and alternative versions. It also covers latency, partial failures, and ecosystem limitations, making the tool fully comprehensible without external documentation.

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?

The input schema covers both parameters with clear descriptions (package name, scoped packages accepted, version defaults to latest), giving 100% coverage. The description adds minimal extra parameter meaning, but the schema carries the load, so baseline 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's purpose: a composite 'should I add this npm package' check that aggregates data from deps.dev and bundlephobia. It gives specific verbs ('fans out', 'returns') and concrete data sources, distinguishing it from sibling research 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 usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small"' and it clarifies ecosystem boundaries ('NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'), effectively telling when not to use this tool and where to go instead.

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