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

The description explains the processing steps (fetch, extract, emit) and the output format ('single text blob ready to drop at site-root/llms.txt'). Annotations already declare readOnly/idempotent/openWorld hints, so the added behavioral details about the pipeline and return value are valuable supplementary context without contradicting the 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 three sentences: the first defines the tool's purpose, the second details the process, and the third lists use cases. It is front-loaded with the most important information and contains no fluff, achieving high density of useful information.

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?

For a tool with only two parameters and no output schema, the description adequately covers the input (URL, optional link limit), the process, the output format, and representative use cases. It lacks only edge-case details like error handling or timeout behavior, which are not critical for this simple tool.

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 comprehensive descriptions for both parameters (url and max_links) with 100% coverage. The description does not add new parameter semantics beyond the schema, but it does reinforce the 'any URL' and 'extracts key links' concepts. Baseline of 3 is appropriate since 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's function: generating a production-ready llms.txt file for any URL. It uses specific verbs and nouns (generate, fetches, extracts, emits) and explicitly mentions the output format, distinguishing it from sibling tools that focus on AI visibility checks or competitor scanning.

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 concrete use cases ('getting a client's site indexed, drafting llms.txt for your own project, auditing how an AI crawler would see a competitor') which clearly indicate when to use this tool. It does not explicitly exclude alternatives or mention sibling tools, but the guidance is sufficient for most scenarios.

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