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

The description adds behavioral context beyond the annotations by explaining the fetch-extract-emit process. The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the description enriches these with specific actions.

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 with no wasted words: first sentence states purpose, second summarizes process, third lists use cases. It is front-loaded and highly scannable.

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 2 well-documented params, comprehensive annotations, and no output schema, the description covers the core behavior and output format ('single text blob'). Minor omission: no mention of error handling or input validation, but adequate for a simple read-only 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 has 100% description coverage for both parameters, so the description adds minimal value. It mentions 'any URL' and 'max 50' but does not elaborate beyond what the schema already provides.

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 states a specific verb ('generate') and resource ('llms.txt file for any URL'), and distinguishes itself from siblings like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing solely on generating the standard llms.txt file.

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 lists three concrete use cases (client indexing, personal projects, competitor auditing), providing clear context. However, it does not explicitly state when not to use this tool or compare to alternative approaches.

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