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fetchSERP

FetchSERP MCP Server

Official
by fetchSERP

get_serp_text

Extract text content from search engine results pages (SERPs) by query, search engine, country, and page count for SEO analysis and keyword research.

Instructions

Get search engine results with text content

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryNoThe country to search from. Default: usus
pages_numberNoThe number of pages to search (1-30). Default: 1
queryYesThe query to search
search_engineNoThe search engine to use (google, bing, yahoo, duckduckgo). Default: googlegoogle

Implementation Reference

  • Handler case for the 'get_serp_text' tool. It invokes the makeRequest helper to call the FetchSERP API endpoint '/api/v1/serp_text' with the provided arguments.
    case 'get_serp_text':
      return await this.makeRequest('/api/v1/serp_text', 'GET', args, null, token);
  • Input schema definition for the 'get_serp_text' tool, specifying parameters like query (required), search_engine, country, and pages_number.
    {
      name: 'get_serp_text',
      description: 'Get search engine results with text content',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'The query to search',
          },
          search_engine: {
            type: 'string',
            description: 'The search engine to use (google, bing, yahoo, duckduckgo). Default: google',
            default: 'google',
          },
          country: {
            type: 'string',
            description: 'The country to search from. Default: us',
            default: 'us',
          },
          pages_number: {
            type: 'integer',
            description: 'The number of pages to search (1-30). Default: 1',
            default: 1,
            minimum: 1,
            maximum: 30,
          },
        },
        required: ['query'],
      },
    },
  • Shared helper method 'makeRequest' that handles API calls to FetchSERP, including authentication, parameter handling, and error management. This is the core logic executed for the 'get_serp_text' tool.
    async makeRequest(endpoint, method = 'GET', params = {}, body = null, token = null) {
      const fetchserpToken = token || process.env.FETCHSERP_API_TOKEN;
      
      if (!fetchserpToken) {
        throw new McpError(
          ErrorCode.InvalidRequest,
          'FETCHSERP_API_TOKEN is required'
        );
      }
    
      const url = new URL(`${API_BASE_URL}${endpoint}`);
      
      // Add query parameters for GET requests
      if (method === 'GET' && Object.keys(params).length > 0) {
        Object.entries(params).forEach(([key, value]) => {
          if (value !== undefined && value !== null) {
            if (Array.isArray(value)) {
              value.forEach(v => url.searchParams.append(`${key}[]`, v));
            } else {
              url.searchParams.append(key, value.toString());
            }
          }
        });
      }
    
      const fetchOptions = {
        method,
        headers: {
          'Authorization': `Bearer ${fetchserpToken}`,
          'Content-Type': 'application/json',
        },
      };
    
      if (body && method !== 'GET') {
        fetchOptions.body = JSON.stringify(body);
      }
    
      const response = await fetch(url.toString(), fetchOptions);
      
      if (!response.ok) {
        const errorText = await response.text();
        throw new McpError(
          ErrorCode.InternalError,
          `API request failed: ${response.status} ${response.statusText} - ${errorText}`
        );
      }
    
      return await response.json();
    }
Behavior2/5

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. While 'Get' implies a read operation, the description doesn't address important behavioral aspects: whether this performs actual web searches (potentially rate-limited), what authentication might be required, what format the 'text content' returns (extracted snippets vs full pages), or any limitations beyond what the parameters suggest. The description is too minimal for a tool that likely makes external API calls.

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 maximally concise - a single 7-word sentence that communicates the core functionality. There's no wasted language or unnecessary elaboration. It's front-loaded with the essential information and doesn't bury key details.

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 complexity of a SERP tool (likely making external API calls, returning structured data) with no annotations and no output schema, the description is insufficient. It doesn't explain what 'text content' means in practice, how results are structured, whether pagination is handled, or any error conditions. For a tool with 4 parameters and likely complex behavior, this minimal description leaves too many questions unanswered.

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?

The input schema has 100% description coverage with clear documentation for all 4 parameters. The description adds no parameter-specific information beyond the tool's overall purpose. It doesn't explain how parameters interact (e.g., how 'country' affects results) or provide usage examples. With complete schema coverage, the baseline score of 3 is appropriate.

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 tool's purpose: 'Get search engine results with text content'. It specifies the verb ('Get'), resource ('search engine results'), and key characteristic ('with text content'). However, it doesn't explicitly differentiate from sibling tools like 'get_serp_results' or 'get_serp_html', which likely provide different output formats.

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. With multiple SERP-related siblings (get_serp_results, get_serp_html, get_serp_ai_mode), there's no indication of what distinguishes this tool's 'text content' output from what those other tools provide. No context about appropriate use cases or exclusions is mentioned.

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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