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DataForSEO MCP Server

DataForSEO MCP Server

by ravinwebsurgeon
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

ai_optimization_llm_models

Retrieve available locations and languages for AI optimization of LLM responses to improve content relevance and targeting.

Instructions

Utility tool for ai_optimization_llm_response to get list of availible locations and languages

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "llm_type": "<llm_type>"
    },
    "name": "ai_optimization_llm_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
llm_typeYestype of llm. Must be one of: 'claude', 'gemini', 'chat_gpt', 'perplexity'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'utility tool' that 'gets list' which implies a read-only operation, but doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what format the output takes. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently states the tool's purpose. It's appropriately sized for a simple lookup tool with one parameter. However, it could be more front-loaded with clearer purpose and could benefit from a second sentence about usage context or output format.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (list format, structure, what 'locations and languages' means), doesn't mention any prerequisites or authentication needs, and doesn't clarify the relationship with the mentioned 'ai_optimization_llm_response' tool. Given the complexity of understanding what locations/languages are being listed and for what purpose, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with one parameter 'llm_type' fully documented in the schema. The description doesn't add any parameter information beyond what's in the schema - it doesn't explain why this parameter is needed or how it affects the results. With high schema coverage, the baseline is 3 even without additional param details in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'gets list of available locations and languages' which is a clear purpose, but it's vague about what exactly is being listed (locations/languages for what?). It mentions 'ai_optimization_llm_response' as context but doesn't fully specify the resource scope. It doesn't clearly distinguish from sibling tools like 'ai_optimization_llm_mentions_locations_and_languages' which appears similar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal guidance - it mentions this is a 'utility tool for ai_optimization_llm_response' which implies some relationship, but doesn't specify when to use this tool versus alternatives. No explicit when/when-not guidance or comparison to sibling tools is provided, leaving the agent to guess about appropriate usage contexts.

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

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