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

The annotations (readOnlyHint: true, idempotentHint: true, destructiveHint: false) already establish the safety profile. The description adds behavioral detail: it fetches the page, extracts title/description/links, emits standard llms.txt markdown, and the output is a single text blob for site-root placement. 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: the first states the core functionality and process, the second lists concise use cases. Every clause adds value, no unnecessary words, and the most important information is front-loaded.

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?

With no output schema, the description explains the output format ('standard llms.txt markdown format' and 'single text blob ready to drop at site-root/llms.txt'). It also covers use cases and parameter behavior. Annotations cover safety, making the tool fully described for selection and invocation.

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 provides 100% coverage for both parameters (url and max_links). The description does not add significant semantic meaning beyond the schema—'any URL' is already implied in the schema's example. Baseline 3 applies because the schema does the heavy lifting.

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 'Generate a production-ready llms.txt file for any URL' and describes the process: fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This specific verb+resource+output distinguishes it from siblings like readability or scan_competitor_ai_presence, which focus on other aspects.

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 provides three use cases: getting a client's site indexed by AI, drafting llms.txt for your own project, and auditing how an AI crawler sees a competitor. This gives clear context for when to use the tool, but it does not mention alternatives or exclusions, 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.