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

Annotations already cover readOnlyHint and openWorldHint, so the safety profile is handled. The description adds genuinely valuable behavioral context the annotations cannot: it is a paid call costing $0.003 USDC via x402, and it enumerates the returned facts. It does not state what happens for a nonexistent package or any rate limits, keeping it out of the top tier.

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?

Front-loaded with the resource and its payload, followed by a short usage directive and the cost note. The field enumeration is dense but each item earns its place since there is no output schema to carry it. Slightly list-heavy but not padded.

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 usefully enumerates what is returned, and the cost disclosure covers the main behavioral unknown. It stops short of covering failure behavior (missing/deprecated packages) and the exact name format, which is the remaining gap for a 2-param lookup 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 50%: 'ecosystem' is documented ('npm (default) or pypi') while 'name' has no description. The description reinforces the ecosystem choice ('npm or PyPI') but adds nothing about the name's expected format (e.g., scoped packages like @scope/pkg, case sensitivity), so it does not compensate for the coverage gap.

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 a specific verb+resource ('npm or PyPI package facts') and then enumerates the exact data points returned (latest version, license, repository, downloads, deprecation, etc.), so an agent knows precisely what this tool produces. Though no sibling covers identical ground, it is unambiguous against adjacent tools like techstack, libdocs, or github.

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?

'Pick healthy dependencies' hints at a use case (dependency vetting), but there is no explicit when-to-use/when-not guidance and no named alternative. The ecosystem selection (npm vs PyPI) is also left to inference rather than tied to a decision rule.

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