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

The annotations already mark it as readOnly and idempotent, but the description adds valuable behavioral context beyond that: it fetches the URL, extracts key content, and emits a standard markdown blob. It also states the output is 'a single text blob ready to drop at site-root/llms.txt,' giving the agent a clear expectation of the result. No contradiction with annotations.

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: a clear first sentence, a breakdown of the process and output, and a short list of use cases. Every sentence adds value without redundancy, and it front-loads the purpose.

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 simplicity (2 parameters, no output schema), the description is remarkably complete. It covers input (any URL), process, output format, and use cases. The lack of an output schema is mitigated by the explicit description of the output as markdown text.

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 100%, with both parameters ('url' and 'max_links') clearly described in the schema. The description does not add additional parameter semantics beyond what the schema provides, so the baseline 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 verb+resource: 'Generate a production-ready llms.txt file for any URL.' It specifies the process (fetches, extracts, emits) and distinguishes itself from sibling tools by focusing on the specific llms.txt format and purpose (AI crawler indexing). This is unambiguous and unique.

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 provides clear context with 'Useful for:' scenarios, covering client work, personal projects, and competitor auditing. However, it does not explicitly state when not to use this tool or mention alternatives, so it falls short of a 5.

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