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ravinwebsurgeon

DataForSEO MCP Server

keywords_data_google_ads_search_volume

Retrieve Google Ads search volume data for keywords to analyze market demand and optimize PPC campaigns based on actual user search behavior.

Instructions

Get search volume data for keywords from Google Ads

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
location_nameNofull name of the location optional field in format "Country" example: United Kingdom
language_codeNoLanguage two-letter ISO code (e.g., 'en'). optional field
keywordsYesArray of keywords to get search volume for

Implementation Reference

  • The async handle method executes the core tool logic: makes a POST request to the DataForSEO Google Ads search volume API endpoint with location, language, and keywords, then validates and formats the response or handles errors.
    async handle(params: any): Promise<any> {
      try {
        const response = await this.dataForSEOClient.makeRequest('/v3/keywords_data/google_ads/search_volume/live', 'POST', [{
          location_name: params.location_name,
          language_code: params.language_code,
          keywords: params.keywords,
        }]);
        return this.validateAndFormatResponse(response);
      } catch (error) {
        return this.formatErrorResponse(error);
      }
    }
  • Defines the Zod schema for input parameters: location_name (optional string), language_code (optional string), and keywords (array of strings).
      getParams(): z.ZodRawShape {
        return {
          location_name: z.string().nullable().default(null).describe(`full name of the location
    optional field
    in format "Country"
    example:
    United Kingdom`),
                  language_code: z.string().nullable().default(null).describe(`Language two-letter ISO code (e.g., 'en').
    optional field`),
          keywords: z.array(z.string()).describe("Array of keywords to get search volume for"),
        };
      }
  • In the KeywordsDataApiModule's getTools method, instantiates GoogleAdsSearchVolumeTool and registers it in the tools record using its getName() as key, along with description, params, and a wrapper around its handle method.
    getTools(): Record<string, ToolDefinition> {
      const tools = [
        new GoogleAdsSearchVolumeTool(this.dataForSEOClient),
    
        new DataForSeoTrendsDemographyTool(this.dataForSEOClient),
        new DataForSeoTrendsSubregionInterestsTool(this.dataForSEOClient),
        new DataForSeoTrendsExploreTool(this.dataForSEOClient),
    
        new GoogleTrendsCategoriesTool(this.dataForSEOClient),
        new GoogleTrendsExploreTool(this.dataForSEOClient),
        // Add more tools here
      ];
    
      return tools.reduce((acc, tool) => ({
        ...acc,
        [tool.getName()]: {
          description: tool.getDescription(),
          params: tool.getParams(),
          handler: (params: any) => tool.handle(params),
        },
      }), {});
    }
Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It states what the tool does but fails to mention critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, data freshness, or what format the search volume data returns. For a data-fetching tool with zero annotation coverage, this is a significant gap.

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

Conciseness5/5

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

The description is a single, clear sentence that efficiently communicates the core function without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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?

Given the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., metrics like monthly searches, competition level), error conditions, or behavioral constraints. For a data retrieval tool with multiple parameters, more contextual information 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%, meaning all parameters are documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions, so it meets the baseline expectation without providing extra value.

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

Purpose4/5

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

The description clearly states the action ('Get') and resource ('search volume data for keywords from Google Ads'), making the purpose immediately understandable. However, it doesn't distinguish this tool from the sibling 'ai_optimization_keyword_data_search_volume', which appears to serve a similar function, preventing a perfect score.

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 no guidance on when to use this tool versus alternatives, particularly the similar-sounding sibling tool. It doesn't mention prerequisites, constraints, or appropriate contexts for invocation, leaving the agent with insufficient usage direction.

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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