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

No annotations are provided, so the description bears the full burden. It discloses a wide range of output details, including health signals and the monetary price ($0.003), which is a valuable behavioral trait. While it doesn't explicitly state 'read-only,' the phrasing 'One call returns' strongly implies a non-mutating lookup.

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 two sentences: a purpose-setting question and a comprehensive but efficient list of returned data. Each clause adds value, and the price is appended succinctly. There is no unnecessary verbosity.

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 effectively enumerates the return data (version, license, downloads, scores, health signals), covering the tool's core value. It omits response structure and error behavior, but the list is detailed enough for an agent to understand what the tool returns.

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?

Both parameters (name and registry) are already described in the input schema, providing 100% coverage and a baseline of 3. The description's statement 'Supports npm and PyPI' simply echoes the schema's enum without adding new semantic detail.

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 purpose-driven question ('Should you depend on this package?') and specifies concrete outputs (version, license, health signals), clearly defining it as a package evaluation tool. It explicitly supports npm and PyPI, distinguishing it from sibling intel tools like domain_intel or intel_company.

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 question 'Should you depend on this package?' establishes a clear decision-making context, and the mention of npm and PyPI indicates applicable registries. It lacks explicit alternatives or exclusions compared to sibling tools, so it doesn't reach a 5.

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