DataForSEO MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' a list, implying a read-only operation, but does not specify whether it requires authentication, has rate limits, returns structured data, or involves any side effects. For a utility tool with zero annotation coverage, this is a significant gap in transparency about its behavior and constraints.

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 directly states the tool's purpose without unnecessary words. It is front-loaded with key information ('utility tool for ai_llm_mentions to get list'), making it easy to understand quickly, and every part of the sentence contributes to clarifying the tool's role.

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 has 0 parameters, no annotations, and no output schema, the description provides basic context but is incomplete. It specifies the tool's purpose and context but lacks details on return format, error handling, or integration with sibling tools. For a utility tool in a complex environment with many siblings, more guidance on output and usage would enhance completeness, though the simplicity of the tool mitigates some gaps.

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?

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not mention any parameters, which is appropriate since none exist. However, it could have clarified that no inputs are needed, but this omission is minor given the context, warranting a high score as it aligns with the schema's lack of parameters.

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 as a 'utility tool for ai_llm_mentions to get list of available locations and languages,' specifying the verb ('get'), resource ('list of available locations and languages'), and context ('for ai_llm_mentions'). However, it does not explicitly differentiate from its sibling tools, such as 'ai_optimization_keyword_data_locations_and_languages,' which may serve a similar purpose for a different context, leaving room for ambiguity.

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 implies usage context ('for ai_llm_mentions'), suggesting it should be used when working with AI LLM mentions data. However, it lacks explicit guidance on when to use this tool versus alternatives, such as 'serp_locations' or other location-related tools in the sibling list, and does not mention any prerequisites or exclusions, leaving usage decisions partially inferred.

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