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

Discloses partial failure behavior, including bundlephobia's first measurement taking 5-30s and the sources_failed field, plus data coverage from both sources. This goes well beyond the annotations (readOnly, openWorld, idempotent, non-destructive) by adding performance and failure semantics.

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?

Although the description is long, every sentence earns its place: purpose, usage triggers, return values, ecosystem scope, and graceful degradation. It front-loads the core purpose and organizes details logically, making the length justified.

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?

With no output schema, the description compensates by listing specific return fields (is_latest, license, bundle sizes, etc.) and noting links, advisories, and alternatives. It also covers failure modes and performance caveats, making it highly complete for a tool with two parameters and no output schema.

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 already fully documents both parameters (package and version, with scoped package support and version default). The description adds no new parameter-specific detail beyond what the schema provides, so a baseline score of 3 is appropriate given 100% schema coverage.

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 as a composite npm package evaluation check ('should I add this npm package'), combining deps.dev and bundlephobia data. It distinguishes itself from siblings by specifying npm-only scope and the exact questions it answers ('is X safe / popular / small').

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?

Explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. Also provides exclusion and an alternative path: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'.

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