DataForSEO MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data is 'estimated usage in AI LLMs' which adds some context about the data source, but doesn't describe rate limits, authentication requirements, response format, pagination, or whether this is a read-only operation. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that gets straight to the point. It's appropriately sized for a data retrieval tool, though it could potentially benefit from slightly more detail given the lack of annotations and output schema.

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 3-parameter tool with 100% schema coverage but no annotations and no output schema, the description provides the basic purpose but leaves significant gaps. It doesn't explain what the return data looks like, any limitations or constraints, or how this tool differs from similar keyword search volume tools. The description is minimally adequate but incomplete for effective agent use.

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 100%, so the schema already fully documents all three parameters. The description doesn't add any additional meaning about the parameters beyond what's in the schema. It doesn't explain what 'search volume data' specifically includes or how the AI LLM usage estimation works. Baseline 3 is appropriate when the schema does the heavy lifting.

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 clearly states the tool's purpose: 'provides search volume data for your target keywords' with the specific context of 'reflecting their estimated usage in AI LLMs'. It uses a specific verb ('provides') and resource ('search volume data'), but doesn't explicitly differentiate from sibling tools like 'keywords_data_google_ads_search_volume' which might serve similar functions.

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?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention any prerequisites, exclusions, or specific contexts where this tool is preferred over sibling tools like 'keywords_data_google_ads_search_volume' or other keyword-related tools in the list.

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