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

The description discloses important behavioral details beyond annotations: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. Annotations already indicate read-only, idempotent, and non-destructive, and the description adds realistic latency and error handling without contradiction.

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 a single dense paragraph, but every sentence is informational. It front-loads the core purpose and fan-out structure, then details return fields and limitations. Could benefit from bullet points for return fields, but remains concise and efficiently structured.

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?

No output schema exists, yet the description fully enumerates return fields (is_latest, license, bundle sizes, dependency count, etc.), explains error handling, lists alternatives, and covers ecosystem scope. It is complete for a tool with only 2 parameters and a composite check.

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 description coverage is 100%, so baseline is 3. The description adds minor semantic value by noting that scoped packages are accepted and default version behavior, but this largely repeats 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 opens with 'Composite 'should I add this npm package to my project' check in ONE call' and specifies fan-out across deps.dev and bundlephobia, clearly stating the tool's unique purpose of assessing npm packages. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on dependency analysis.

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' or 'what does adding lodash cost me'.' It also notes ecosystem limitations ('NPM ecosystem only in v1') and directs other ecosystems to a different tool, offering clear context on when not to use.

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