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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context beyond those: it 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also states the output is a single text blob, which clarifies what the agent will receive. No contradiction 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 compact yet information-dense: it states the core function, the process, the output format, and concrete use cases in three sentences. Every sentence earns its place, and the most important information (what it does) is front-loaded. No fluff or redundancy.

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 only two parameters and no output schema, the description covers all necessary context: input URL, optional max_links (implied), the fetching process, output format (single text blob), and even typical use cases. The tool's behavior is fully described for an agent to invoke it correctly.

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 description coverage is 100%, so the baseline is 3. The description does not add parameter-specific meaning beyond what the schema already provides. It mentions 'key links' and the output format, but the individual parameters (url and max_links) are sufficiently documented in the schema itself.

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 opens with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL.' It clearly distinguishes itself from sibling tools by focusing on llms.txt generation and AI crawler indexing, which none of the listed siblings cover. The detailed breakdown of fetching and extracting content reinforces the unique purpose.

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 clear 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 gives concrete when-to-use context. It does not explicitly mention when not to use or contrast with alternatives, but the use cases are sufficiently specific for an agent to decide.

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