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

The description discloses the tool's behavior: fetches the page, extracts title/description/key links, and emits standard markdown format. This adds value beyond the annotations (readOnlyHint, idempotentHint, etc.) by explaining the actual operations and the output nature. 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 three well-structured sentences. The first sentence states the main purpose and target audience, the second explains the process, and the third lists use cases. No redundant or superfluous 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?

Given the tool's low complexity (2 parameters, simple fetch+extract), the description covers the purpose, process, output format, and use cases comprehensively. Even without an output schema, the description clearly communicates what the tool returns.

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%, but the description adds extra context such as the default value of 25 and max of 50 for max_links, and explains that the output is a single text blob ready to drop at site-root/llms.txt. This goes beyond the schema's basic type and description.

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: generating a production-ready llms.txt file for any URL. It specifies the action (generate), the output format (standard llms.txt markdown), and distinguishes from siblings like scan_competitor_ai_presence by focusing on AI crawler indexing.

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 lists three use cases: getting a client's site indexed, drafting your own llms.txt, and auditing competitor visibility. It provides clear context on when to use, though it does not explicitly mention when not to use or name alternative tools.

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