audit_dependencies

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

No annotations are provided, so the description carries the full burden. It states that it 'Reads requirements.txt and/or pyproject.toml' and 'Returns per-dependency status, latest versions, and CVE/vulnerability details,' implying a read-only, non-mutating operation. However, it does not mention potential network calls or side effects, so it is not fully transparent.

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 concise and well-structured, with the main purpose front-loaded. It contains no unnecessary information and is easy to understand.

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?

The description gives a good overview of what the tool does and what it returns, but not the exact output format. Since no output schema is provided, this is sufficient for a simple tool. It is complete enough for most use cases.

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?

The schema has one parameter (path) with a clear description, so schema coverage is 100%. The tool description does not add additional meaning beyond the schema, so the 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The description clearly states the tool's purpose: 'Audit Python project dependencies for outdated packages and known vulnerabilities.' It is specific and distinct from the sibling tools, which focus on other aspects of code quality.

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 implies usage by stating what it does, but it does not explicitly mention when to use it or provide alternatives. It is clear enough that it is for dependency auditing, but there is no direct guidance on when to choose it over other tools.

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