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

Annotations already declare readOnly, openWorld, idempotent, not destructive. The description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia first measurement may take 5-30s, and sources_failed lists timeouts. No contradiction with 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 a single paragraph of 6 sentences, front-loaded with the main purpose. It is dense but well-structured, with each sentence adding value. Could be slightly more scannable with bullet points, but still concise.

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 thoroughly explains the return shape (summary block with fields, per-advisory detail, links, alternative versions) and covers edge cases (partial failures, scoped packages, performance caveats). Complete for a 2-parameter read tool.

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 clear descriptions for both parameters (package name with scoped support, version with default to latest). The description does not add significant extra meaning beyond the schema, so a 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 performs a composite check for adding an npm package, combining deps.dev and bundlephobia data. It specifies the verb 'scan dependency' and resource 'npm package', distinguishing it from sibling tools like scan_competitor_ai_presence.

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 states when to use: whenever an agent asks about safety, popularity, size, or cost of adding a package. It also notes the NPM-only scope and directs other ecosystems to deps.dev:version, providing clear usage context. No explicit when-not-to-use, but the guidance is sufficient.

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