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

Beyond annotations (readOnly, idempotent), description discloses composite call behavior, partial failure degradation, and bundlephobia's first measurement delay (5-30s). It adds valuable context about sources_failed reporting, which annotations do not cover. No contradictions.

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?

Description is front-loaded with main purpose and use cases, then details return structure, ecosystem limitations, and failure behavior. All sentences are informative, though slightly verbose. Could tighten but structure is logical and effective.

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, description fully explains return format (summary block, advisories, links, alternatives) and handles complexity like partial failures and performance delays. Adequate for agent to understand tool capabilities and expectations.

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 covers both parameters with descriptions (package name, version with default). Description adds minor context (scoped packages accepted, version defaults to latest), but schema coverage is 100%, so baseline of 3 is appropriate. No significant added meaning beyond schema.

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?

Description clearly states the tool is a composite check for npm packages using deps.dev and bundlephobia, specifying the exact resource and action. It implicitly distinguishes from sibling tools by its unique focus on npm dependency analysis, setting it apart from other 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?

Description explicitly provides when to use ('is X safe / popular / small', 'what does adding lodash cost me') and when not to use ('NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly'), offering clear context and alternatives.

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