MCP User Data Enrichment Server

A Model Context Protocol (MCP) server that enriches user data by adding social network links. This server can be integrated with AI platforms like Smithery.ai to provide social media link discovery capabilities.

# Features

• User Data Enrichment: Takes user information (name, birth date) and returns social media links
• Mock Data Support: Includes pre-configured social links for demonstration
• Dynamic Generation: Automatically generates social links for new users
• MCP Protocol: Standard MCP implementation via stdio
• HTTP Wrapper: Optional HTTP API for remote access
• Smithery Integration: Ready for integration with Smithery.ai

# Installation

npm install mcp-user-data-enrichment

# Usage

# # As MCP Server (Recommended for Smithery)

#  Direct stdio usage
node src/mcp-server.js

#  Or via npm script
npm run mcp

# # As HTTP Server

#  Start HTTP server on port 3000
npm start

# API Endpoints

# # HTTP API (when running as server)

• GET /status - Server status
• GET /tools - List available tools
• POST /tools/call - Call any tool
• POST /enrich-user - Enrich user data

# # MCP Protocol

The server provides one tool: enrich_user_data

Input Schema:

{
  "firstName": "string",
  "lastName": "string", 
  "birthDate": "string (YYYY-MM-DD)"
}

Output:

{
  "user": {
    "firstName": "John",
    "lastName": "Smith",
    "birthDate": "1990-01-01"
  },
  "socialLinks": {
    "instagram": "https://instagram.com/john_smith",
    "facebook": "https://facebook.com/john.smith",
    "twitter": "https://twitter.com/john_smith",
    "linkedin": "https://linkedin.com/in/john_smith"
  }
}

# Smithery.ai Integration

This MCP server is designed to work with Smithery.ai, a platform for AI agent orchestration.

# # Setup in Smithery

1. Deploy your server to a public repository on GitHub
2. Configure MCP connection in Smithery:

   {
     "mcpServers": {
       "user-data-enrichment": {
         "command": "node",
         "args": ["path/to/mcp-server.js"]
       }
     }
   }
   

3. Use the tool in your AI agent workflows

# # Example Smithery Usage

// In your Smithery agent
const result = await mcp.callTool('enrich_user_data', {
  firstName: 'John',
  lastName: 'Smith', 
  birthDate: '1990-01-01'
});

console.log(result.content[0].text);

# Development

#  Install dependencies
npm install

#  Run in development mode
npm run dev

#  Test MCP server directly
echo '{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}' | node src/mcp-server.js

# Testing

#  Run test client
node test-client.js

#  Test with curl
curl -X POST http://localhost:3000/enrich-user \
  -H "Content-Type: application/json" \
  -d '{"firstName": "John", "lastName": "Smith", "birthDate": "1990-01-01"}'

# Mock Data

The server includes mock social links for these users:

• John Smith
• Sarah Johnson
• Michael Brown

For other users, links are generated automatically based on the name.

# Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

# License

MIT License - see LICENSE file for details

# Deployment Files

• Dockerfile - Docker configuration for containerized deployment
• smithery.yaml - Smithery.ai configuration file
• .dockerignore - Docker ignore file for optimized builds

# Related Links

• Model Context Protocol
• Smithery.ai - AI Agent Orchestration Platform
• MCP Inspector - MCP Testing Tool