validate_ai_readmes

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

No annotations are present, so the description must disclose all behavioral traits. It mentions what is checked (token count, structure, content quality) and that results include suggestions, but it does not state whether the tool modifies files, requires permissions, or has side effects. The read-only nature is implied but not explicit.

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 exceptionally concise: two sentences that efficiently convey the tool's purpose and output without any superfluous content.

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?

Given the tool's complexity (nested config, no output schema), the description is incomplete. It lacks details about return value structure, validation failure behavior, or how suggestions are presented. The agent would need more context to correctly handle the tool's output.

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 description coverage is 100%, so parameters are already well-documented in the schema. The description adds minimal extra meaning beyond stating the overall purpose; it does not elaborate on parameter details. Thus 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's purpose: validating AI_README.md files in a project, checking token count, structure, and content quality. It uses a specific verb ('validate') and resource ('AI_README.md files'), and distinguishes itself from sibling tools like compress, discover, or update.

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 the tool should be used to check the quality of readme files, but it does not provide explicit guidance on when to use it versus alternatives (e.g., after updates, before compression). No when-not-to-use or exclusions are mentioned.

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