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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safe-read profile is covered. The description adds the scope of the dataset (AI-readiness / llms.txt / robots.txt) but says nothing about pagination, defaults, or the meaning of facet values.

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?

A single dense sentence that front-loads the core action and scoping. It is efficient with no filler, though it packs several facet examples without organizing them.

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 31-parameter tool with a required facet argument and a rich output schema, the description is adequate but thin: it does not mention the required facet, the allowed facet values, or how filters combine, leaving the schema to carry the load.

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%, so all 31 parameters are documented in the schema itself. The description only restates the facet dimensions already implied by the schema and adds no syntax, format, or interaction detail beyond it.

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?

States a specific verb ('Return facet counts') and resource ('AI-readiness dataset') and enumerates the aggregation dimensions (producer, generator, TLD, category, blocked crawlers). It is clearly a facet/aggregation tool, though it never names or distinguishes itself from the sibling datasets_llms_search or datasets_llms_item.

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 gives no when-to-use guidance and does not point to alternatives such as datasets_llms_search for row-level results. The reader must infer that this is for aggregate counts rather than record retrieval.

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