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

With no annotations, the description carries the full burden of disclosing behavior. It states what data is included but does not reveal whether it is read-only, how recent the statistics are, whether any parameters or filters exist, or what the response structure looks like. The colon-led list of statistics is content, not behavioral transparency.

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 a single concise sentence that leads with the core concept ('corpus-level statistics') and then lists specific examples. There is no filler or redundant language, making it easy to parse.

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 parameterless tool, the description gives enough to understand what kind of data comes back, but it omits an explicit return format and does not clarify the scope of 'measured domains' or any access prerequisites. The missing output schema means the description could reasonably be expected to say more about the result shape.

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 tool has zero parameters, so the schema fully covers any input concerns. The description adds value by explaining what the resulting statistics measure, which complements the empty schema without needing to compensate for undocumented parameters.

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 names the resource ('corpus-level statistics') and specifies three concrete metrics: per-crawler blocking counts, mean access score, and llms.txt adoption. This gives an agent a clear idea of what the tool computes, though it does not explicitly distinguish itself from the similarly named sibling census_facts.

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 phrase 'corpus-level statistics' implies this is for aggregate data, but there is no explicit statement of when to use this tool versus alternatives like census_facts or scan_site. No when-not conditions or alternative names are provided.

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