ai_keyword_demand

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

Goes well beyond annotations: discloses that this is a SANDBOX endpoint returning dummy data with no payment, that values are statistical estimates derived from 'People Also Ask' data, and that no real prompt counts from ChatGPT/Gemini/Perplexity are reported. That methodological caveat is exactly the kind of context an agent needs before quoting numbers to a user. It does not cover rate limits or response shape.

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-loads the core capability, then the sandbox caveat, then the interpretation caveat — a sensible order for an agent scanning it. Slightly redundant in repeating 'modeled/estimated' three times and in spelling out the full DataForSEO source name, but nearly every sentence carries decision-relevant information.

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?

With no output schema, the description still conveys what comes back (a 12-month demand history per keyword) and how to treat it (relative, not absolute). Missing only a note on response structure/units and on the unsupported-language refusal behavior that the schema hints at.

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 keywords, language_code and location_code are already fully documented with limits and codes. The description only restates the 10-keyword cap and 12-month window, adding nothing about location/language semantics. Baseline 3 is appropriate when the schema does the work.

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+resource: modeled AI-search demand for up to 10 keywords with 12-month history, and names the upstream source (DataForSEO AI Optimization Keyword Search Volume). It implicitly separates itself from keyword_metrics by scoping to AI search rather than classic search volume, though it never names that sibling explicitly.

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?

Offers one real usage instruction — 'Use it as a relative demand indicator' — which tells the agent how to interpret output but not when to pick this over keyword_metrics or keyword_ideas. No exclusions or alternative routing are given, so the agent must infer usage from the name alone.

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