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

Goes well beyond the readOnly/openWorld annotations by disclosing cost behavior (pay-as-you-go, higher cost with web_search), a hard prompt limit of 500 characters, the fixed-model mapping per provider, and the perplexity override where web_search is ignored. It also warns that empty citations are expected without web search and must not be read as proof of missing visibility, which prevents a serious misinterpretation.

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?

Front-loaded with the core action, then layered by parameter and caveat; nearly every sentence adds operational value. It is somewhat dense in a single block, and the instruction to name the concrete model in the report is agent-guidance rather than tool semantics, but nothing is truly wasted.

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?

Covers what an agent needs to call it correctly: purpose, all four parameters, cost model, prompt length cap, provider quirks, and how to interpret citations. The return-field list duplicates the existing output schema, but that redundancy is harmless given how caveat-heavy the citation behavior is.

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 carries the full burden and does so: model gets enum values plus the concrete underlying model per alias, prompt gets a 500-character limit, web_search gets default and cost semantics plus the perplexity exception, and workspace gets the slug meaning and its optionality condition.

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?

Names a specific verb and resource: it sends a prompt directly to an LLM via DataForSEO and returns the answer text. That clearly separates it from the metric-style LLM siblings (dfs_llm_mentions, dfs_llm_top_domains), though those siblings are never named as alternatives.

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?

Usage guidance exists but is parameter-level rather than tool-level: web_search=True is indicated when cited sources matter, and workspace is described as optional with a single workspace. There is no explicit statement of when to use this tool versus dfs_llm_mentions or dfs_llm_mentions_metrics for visibility analysis.

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