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

The description discloses key behavioral traits beyond annotations: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. It also notes the output is a single text blob. Given the read-only, idempotent annotations, this additional process detail is valuable. It does not mention potential network errors or rate limits, but that is a minor gap for a read-only tool.

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 three well-structured sentences: purpose, process, and use cases. It is front-loaded with the core function, every sentence adds value, and there is no redundant text. It is appropriately sized for a tool with two parameters and a clear output.

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 covers the tool's purpose, process, output format, and common use cases. Since there is no output schema, the mention of 'a single text blob ready to drop at site-root/llms.txt' is helpful. It doesn't address edge cases like invalid URLs or fetch failures, but for a simple read-only tool with robust annotations, the description is largely complete.

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 input schema has 100% coverage, with url and max_links already described clearly. The description adds little beyond the schema: it mentions 'any URL' and 'key links' but doesn't elaborate on parameter defaults or constraints. Since the schema fully covers the parameters, the baseline of 3 is appropriate; the description doesn't provide extra nuance.

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 function: 'Generate a production-ready llms.txt file for any URL.' It specifies the verb (generate), resource (llms.txt), and scope (any URL), and details the process (fetches, extracts, emits). This distinguishes it from sibling tools like ai_visibility_check or deep_research, as its focus is on producing a specific file format.

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 explicitly lists several use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' This provides clear context for when to use the tool, though it doesn't mention exclusions or alternative tools. The guidance is helpful but not as explicit as a direct comparison to siblings.

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