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

The description discloses that the tool 'Fetches the page, extracts title/description/key links' and 'emits the standard llms.txt markdown format', adding behavioral context beyond the annotations' readOnlyHint and idempotentHint. This informs the agent about network fetching and output format, which is valuable.

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 sentences, front-loaded with the primary action, and every sentence earns its place: the first states the core function, the second explains the process, and the third lists use cases. No wasted words.

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?

Despite having no output schema, the description adequately explains the return value ('Output is a single text blob ready to drop at site-root/llms.txt') and the internal process. It is sufficiently complete for a simple read-only tool, though it does not cover edge cases like invalid URLs.

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 already provides detailed descriptions for both parameters (url and max_links) with 100% coverage. The description reinforces the 'any URL' aspect but does not add additional parameter semantics beyond what the schema already states, so 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: 'Generate a production-ready llms.txt file for any URL'. It uses a specific verb ('Generate') and resource ('llms.txt'), and distinguishes itself from sibling tools like scan_competitor_ai_presence and ai_visibility_check by focusing on file generation rather than auditing.

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 explicit use cases ('Useful for: 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'), giving clear context for when to use. It does not explicitly mention when-not-to-use or name 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.