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

Annotations declare destructiveHint=true and readOnlyHint=false, but the description adds substantial behavioral context: the endpoint is 'among the most expensive endpoints in this provider' with a measured cost, the `target` field rejects bare strings and arrays of strings, a rejected request still returns HTTP 200, and pagination uses `search_after_token` not `offset` past the first pages. This goes far beyond what annotations provide.

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 dense but front-loaded with the core purpose, then covers cost, target format, envelope, and sibling routing. Every sentence adds value. It is longer than ideal, but the length is justified by the high-stakes cost warning and the non-obvious `target` format. The structure is logical: purpose → return → cost → parameter warning → envelope → alternative.

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 complexity (1 body parameter with many nested fields), the description covers the essential operational facts: cost, target format, envelope parsing, pagination, and the sibling for counts. The output schema exists, so return values need not be enumerated. The description is complete enough for an agent to call this correctly and avoid the most common pitfalls.

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 0%, so the description must compensate. It does explain the critical `target` parameter shape ('array of objects, each `{"domain": "..."}` or `{"keyword": "..."}`') and the filter source. However, it does not enumerate the other parameters (limit, offset, platform, language_code, etc.), leaving the agent to read the schema for those. The description adds meaning for the most error-prone parameter but not all.

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 opens with a specific verb and resource: 'The individual answers in which an assistant mentioned your target.' It clearly distinguishes this from the sibling `post_dataforseo_ai_llm_mentions_aggregated_metrics_live` by stating 'For counts rather than the mentions themselves use...' This is a precise, non-tautological statement of what the tool returns.

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 names the alternative for counts (`post_dataforseo_ai_llm_mentions_aggregated_metrics_live`) and states the condition for choosing it. It also warns about the `target` parameter format, the envelope structure, and the pagination behavior. This is explicit when/when-not guidance.

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