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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds behavioral context: it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown format. It also states the output is a single text blob ready for deployment. No contradictions 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 two sentences with a bulleted list of use cases. It is front-loaded with the core action ('Generate a production-ready llms.txt file...') and provides all necessary information without redundancy. Every sentence contributes to understanding.

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 params, no output schema, no nested objects), the description is fully complete. It covers purpose, usage, behavior, and output format. It does not need to describe return values since no output schema exists, and it clearly states the output is a markdown text blob.

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%, so parameters are fully documented in the schema. The description adds meaning by explaining that the 'url' parameter is the site to summarize and that the tool extracts title/description/key links. It does not explicitly mention 'max_links', but the schema covers it with a default and max value. The description adds value beyond the raw schema.

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 generates a 'production-ready llms.txt file' for a given URL, specifically targeting AI crawlers like ChatGPT, Claude, and Perplexity. It uses specific verbs ('generate', 'fetches', 'extracts', 'emits') and a clear resource (llms.txt file). The tool is distinct from sibling tools, which focus on weather, betting, research, etc.

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 lists explicit use cases: getting a client's site indexed, drafting for own projects, auditing competitors. It provides clear context for when to use the tool. However, it does not explicitly state when not to use it or provide alternatives, though the uniqueness of the tool makes exclusions less necessary.

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