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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses the operational behavior: it 'fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format'. It also specifies the output is a 'single text blob ready to drop at site-root/llms.txt', providing more context than annotations alone. 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 concise (three sentences) and well-structured, front-loading the primary purpose, then explaining the process and output, and ending with concrete use cases. Every sentence earns its place with no 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?

For a tool with 2 parameters and no output schema, the description is complete: it explains what the tool does, how it works, the output format, and when to use it. The absence of an output schema is compensated by the explicit 'single text blob' statement. The annotations further cover safety traits, so no critical information is missing.

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%, with both 'url' and 'max_links' already clearly described. The description adds minimal semantic value beyond the schema—it mentions 'any URL' and 'key links' but does not elaborate on parameter details or defaults. Baseline 3 is appropriate when the schema carries the burden.

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 with a specific verb and resource: 'Generate a production-ready llms.txt file for any URL'. It further details the process (fetches page, extracts title/description/key links) and the output format, making it unmistakable from sibling tools like 'ai_visibility_check' or 'scan_competitor_ai_presence'.

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 the tool. It does not mention exclusions or alternatives, but the use cases are sufficiently specific for a simple tool.

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