mcp-server_client

AneshSM/mcp-server_client

3.1

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This repository implements a Model Context Protocol (MCP) server using Node.js and TypeScript.

Tools
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Resources
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Prompts
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MCP Server & Client Integration

This repository implements a Model Context Protocol (MCP) server and client using Node.js and TypeScript, demonstrating how to create AI-enabled applications with structured tool access and data integration.

🎯 What is Model Context Protocol (MCP)?

  • Standardized protocol for AI applications to access external data and tools
  • JSON-RPC based communication between clients and servers
  • Enables AI models to interact with databases, APIs, and other resources
  • Secure and structured way to extend AI capabilities

🏗️ Project Structure

mcp-server_client/
├── src/
│   ├── server.ts          # MCP Server implementation
│   ├── client.ts          # MCP Client with AI integration
│   └── data/
│       └── users.json     # Sample data storage
├── build/                 # Compiled JavaScript output
├── package.json          # Dependencies and scripts
├── tsconfig.json         # TypeScript configuration
└── MCP_Integration_Presentation.md  # Detailed presentation

🔧 Technology Stack

Core Dependencies

  • @modelcontextprotocol/sdk - Official MCP SDK
  • TypeScript - Type safety and modern JavaScript
  • Node.js - Runtime environment with ES modules
  • Zod - Schema validation for tool parameters

AI Integration

  • @ai-sdk/google - Google Gemini AI integration
  • ai - AI SDK for tool execution
  • @inquirer/prompts - Interactive CLI prompts
  • tsx - Development runtime for TypeScript

🚀 Quick Start

1. Installation

npm install

2. Environment Setup

Create a .env file with your API key:

GEMINI_API_KEY=your_gemini_api_key_here

3. Development Workflow

# Build TypeScript
npm run build

# Run server in development mode
npm run server:dev

# Run client (in another terminal)
npm run client:dev

# Debug with MCP inspector
npm run server:inspect

📡 Available Capabilities

🛠️ Tools

  • example - Echo tool for testing MCP functionality
  • create-user - Create new users with validation
  • create-random-user - Generate users with AI assistance

📚 Resources

  • users://all - Retrieve all users from the database
  • users://{userId}/profile - Get specific user details

💭 Prompts

  • generate-dummy-user - AI prompt for user profile generation

🔄 How It Works

Server-Client Flow

User Input → Client → AI Model → Tool Selection → MCP Server → Data Storage
     ↑                                                            ↓
Response ← Client ← AI Processing ← Tool Response ← MCP Server ← Data Retrieval

Example Interaction

  1. User: "Create a user named John Doe"
  2. Client sends query to Gemini AI
  3. AI selects create-user tool
  4. MCP server validates and stores user data
  5. Response returned to user

🛠️ Available Scripts

  • npm run build - Compile TypeScript to JavaScript
  • npm run build:watch - Watch mode compilation
  • npm run server:dev - Start development server with auto-reload
  • npm run server:inspect - Start server with MCP inspector
  • npm run client:dev - Run interactive client
  • npm run client:start - Run compiled client

🔍 Development Tools

MCP Inspector

The inspector provides a web interface for:

  • Interactive tool testing
  • JSON-RPC message inspection
  • Protocol compliance validation
  • Real-time debugging

Access via: npm run server:inspect

Client Interface

The client offers multiple interaction modes:

  • Query - Natural language AI-powered queries
  • Tools - Direct tool execution with parameter input
  • Resources - Data access and retrieval
  • Prompts - AI prompt generation and execution

🎯 Claude Desktop Integration

Configure Claude Desktop to use your MCP server:

// %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "mcp-server": {
      "command": "node",
      "args": ["C:\\path\\to\\your\\project\\build\\server.js"]
    }
  }
}

🛡️ Security & Best Practices

  • ✅ Input validation with Zod schemas
  • ✅ Error handling with comprehensive try-catch blocks
  • ✅ Type safety throughout the application
  • ✅ API key management with environment variables
  • ✅ Structured logging (stderr for logs, stdout for protocol)

📚 Key Features

AI-Powered Operations

  • Natural language to tool execution
  • Context-aware responses
  • Multi-step reasoning capabilities

Type-Safe Development

  • Full TypeScript integration
  • Schema validation for all inputs
  • Compile-time error checking

Extensible Architecture

  • Easy addition of new tools
  • Flexible resource definitions
  • Modular prompt system

🔮 Extension Examples

Add new tools easily:

server.tool(
  "custom-tool",
  "Description of your tool",
  {
    param: z.string().describe("Parameter description"),
  },
  async ({ param }) => {
    // Your tool logic here
    return { content: [{ type: "text", text: "Result" }] };
  }
);

📖 Documentation

For a comprehensive guide including architecture details, advanced features, and integration patterns, see:

  • - Complete technical presentation

🔗 Resources

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Add your enhancements
  4. Test with MCP inspector
  5. Submit a pull request

📄 License

ISC License - see package.json for details

Built with using Model Context Protocol