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

Annotations already indicate idempotent and read-only behavior. The description adds operational detail: fetching the page, extracting title/description/key links, and emitting standard markdown. This complements annotations without contradiction.

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: two sentences that state the action, output, and use cases. Every sentence is purposeful and front-loaded, 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?

Given the simple tool (2 params, no output schema), the description covers the output format, use cases, and basic behavior. It lacks error handling or limitations (e.g., public URL required), but overall it's adequate for proper tool invocation.

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 coverage is 100% and already documents both parameters. The description doesn't add new information about parameters; it only reiterates the URL usage. Baseline score is appropriate as the description provides no extra semantic value.

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 defines the tool's function: generate a llms.txt file for a URL. It specifies the output format and distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by focusing solely on llms.txt generation.

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 (e.g., client indexing, personal project, competitor audit) but does not specify when not to use this tool or mention alternatives. The examples give clear context for appropriate usage.

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