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fetchSERP

FetchSERP MCP Server

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by fetchSERP

generate_wordpress_content

Create AI-driven WordPress content with customizable prompts and models. Input user and system prompts to generate tailored posts using the FetchSERP MCP Server.

Instructions

Generate WordPress content using AI with customizable prompts and models

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ai_modelNoThe AI model (default: gpt-4.1-nano)gpt-4.1-nano
system_promptYesThe system prompt
user_promptYesThe user prompt

Implementation Reference

  • Handler implementation for the 'generate_wordpress_content' tool. It calls the shared makeRequest method to proxy the tool arguments to the external FetchSERP API endpoint '/api/v1/generate_wordpress_content'.
    case 'generate_wordpress_content':
      return await this.makeRequest('/api/v1/generate_wordpress_content', 'GET', args, null, token);
  • Input schema for the generate_wordpress_content tool, defining required user_prompt and system_prompt, optional ai_model.
    inputSchema: {
      type: 'object',
      properties: {
        user_prompt: {
          type: 'string',
          description: 'The user prompt',
        },
        system_prompt: {
          type: 'string',
          description: 'The system prompt',
        },
        ai_model: {
          type: 'string',
          description: 'The AI model (default: gpt-4.1-nano)',
          default: 'gpt-4.1-nano',
        },
      },
      required: ['user_prompt', 'system_prompt'],
    },
  • index.js:462-484 (registration)
    Registration of the generate_wordpress_content tool in the MCP server's listTools response, including name, description, and schema.
    {
      name: 'generate_wordpress_content',
      description: 'Generate WordPress content using AI with customizable prompts and models',
      inputSchema: {
        type: 'object',
        properties: {
          user_prompt: {
            type: 'string',
            description: 'The user prompt',
          },
          system_prompt: {
            type: 'string',
            description: 'The system prompt',
          },
          ai_model: {
            type: 'string',
            description: 'The AI model (default: gpt-4.1-nano)',
            default: 'gpt-4.1-nano',
          },
        },
        required: ['user_prompt', 'system_prompt'],
      },
    },
  • Shared helper function makeRequest that performs authenticated HTTP requests to the FetchSERP API base URL, used by the generate_wordpress_content handler and other tools.
    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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions AI generation but lacks details on permissions, rate limits, output format, or potential side effects. For a tool that likely creates content, this is a significant gap in transparency.

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, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function.

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 inadequate. It doesn't explain what the generated content looks like, how it's delivered, or any behavioral traits, leaving critical gaps for an AI agent to understand the tool's full context.

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%, so the schema already documents all parameters. The description adds minimal value by mentioning 'customizable prompts and models', which loosely maps to the parameters but doesn't provide additional syntax or usage details beyond what the schema provides.

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 ('Generate WordPress content using AI') and specifies the resource ('WordPress content'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'generate_social_content', which might be a similar AI generation tool for different content types.

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. It doesn't mention prerequisites, ideal scenarios, or exclusions, leaving the agent with no contextual cues for selection among the many sibling tools, including other content generation tools.

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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