Example MCP Server Built on Next.js: Universal Adapter for AI Applications

Overview: What is Example MCP Server?

The Example MCP Server built on Next.js serves as a critical bridge connecting AI applications and their underlying tools, data sources, and resources through the Model Context Protocol (MCP). MCP acts as a standardized interface protocol similar to USB-C for modern devices. This server facilitates seamless integration between sophisticated AI applications like Claude Desktop, Continue, Cursor, and others, enabling them to access specific context, tools, and endpoints required for their operations.

🔧 Core Features & MCP Capabilities

The Example MCP Server integrates seamlessly with various AI clients, providing a robust foundation for modern AI workflows. It supports key features such as resource management, tool integration, prompt handling, and dynamic configuration via the MCP protocol. Specifically, this server is designed to be deployable on Vercel, leveraging Fluid Compute functions for efficient execution. The documentation provided outlines how to customize and run the server with your tools and resources.

⚙️ MCP Architecture & Protocol Implementation

Protocol Flow Diagram

The flow of data within the Example MCP Server can be visualized using a Mermaid diagram:

graph TD;
    A[AI Application] -->|MCP Client| B[MCP Protocol];
    B --> C[MCP Server];
    C --> D[Data Source/Tool];
    style A fill:#e1f5fe;
    style C fill:#f3e5f5;
    style D fill:#e8f5e8;

This diagram illustrates the interaction between an AI application, MCP client, this server, and the final resource or tool. The protocol ensures that data flows smoothly between these entities, allowing for dynamic updates and context-aware operations.

Data Architecture Diagram

The following diagram details the internal architecture of the Example MCP Server:

graph TD;
    A[AI Application] --> B[MCP Client];
    B --> C[MCP Protocol];
    C --> D[MCP Server];
    D --> E[Data Source/Tool];
    F[Configuration] --> D;
    style A fill:#e1f5fe;
    style B fill:#f3e5f5;
    style C fill:#f0f8ff;
    style D fill:#f7f2eb;
    style E fill:#e8f5e8;

This architecture highlights the communication pathways and data flow, providing insight into how configuration settings are managed and applied.

🚀 Getting Started with Installation

To get started with your MCP server setup on Next.js, follow these steps:

1. Update app/mcp.ts: Customize this file to include your specific tools, prompts, and resources based on the documentation provided in the MCP TypeScript SDK.
2. Install Dependencies: Ensure you have Node.js and npm installed. Run npm install to set up your development environment.
3. Configure Environment Variables: Set up the necessary environment variables, such as REDIS_URL, in your .env.local file.
4. Deploy with Vercel:
   • Use the template provided by Vercel: Deploy next.js mcp template
   • Enable Fluid Compute to optimize performance for function execution.

💡 Key Use Cases in AI Workflows

The Example MCP Server can be used in several key AI workflows, including but not limited to:

1. Custom Prompt Generation: Utilize the app/mcp.ts file to define custom prompts that contextually enhance AI interactions.
2. Data Source Integration: Integrate various data sources such as APIs or databases directly into your AI application through this MCP server.

By leveraging these capabilities, developers can build more sophisticated and versatile AI applications without requiring deep technical knowledge of underlying infrastructure.

🔌 Integration with MCP Clients

The Example MCP Server is fully compatible with the following MCP clients:

• Claude Desktop: Supports full API key authentication.
• Continue: Fully supports resources and tools but requires specific setup for prompts.
• Cursor: Supports tools, but not all features are currently available due to protocol limitations.

For detailed setup instructions, refer to the MCP TypeScript SDK documentation.

📊 Performance & Compatibility Matrix

The following table outlines compatibility and performance details for different MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🛠️ Advanced Configuration & Security

Setting Up MCP Configurations

Here's a configuration snippet for setting up the Example MCP Server:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This sample configuration demonstrates how to register an MCP server with specific arguments and environment variables.

Security Best Practices

• Environment Variables: Ensure sensitive information like API keys is stored securely in environment variables.
• Authentication Mechanisms: Implement secure authentication mechanisms for clients accessing the server’s endpoints.

❓ Frequently Asked Questions (FAQ)

1. Q: How do I integrate my custom tools and resources with the Example MCP Server?
   • A: Customize app/mcp.ts to define your tools, prompts, and resources according to the provided documentation.
2. Q: Can I use this server with applications other than those listed in the compatibility matrix?
   • A: While primarily tested with Claude Desktop, Continue, and Cursor, the server can be adapted for additional clients through minor modifications.
3. Q: How do I optimize performance when running Fluid Compute on Vercel?
   • A: Adjust the max duration setting in app/sse/route.ts to 800 if you're using a Vercel Pro or Enterprise account to ensure efficient execution.
4. Q: Are there any known limitations with the current setup that I should be aware of?
   • A: Cursor currently lacks full support for prompts, and only tools are available for integration.
5. Q: How can I ensure my MCP server is secure against unauthorized access and breaches?
   • A: Use strong authentication mechanisms and securely manage environment variables to protect your server from unauthorized access.

👨‍💻 Development & Contribution Guidelines

Contributors are encouraged to:

• Submit pull requests for bug fixes or new features.
• Follow the coding style and conventions outlined in the existing repository.
• Ensure thorough testing of any changes before submitting a PR.

To contribute, clone this repository, make your changes, and push them. After pushing your code, create a pull request describing your contribution clearly.

🌐 MCP Ecosystem & Resources

For more information on Model Context Protocol (MCP), visit the official documentation and resources at:

• ModelContextProtocol GitHub Repository
• MCP TypeScript SDK Documentation

Join the community for updates, discussions, and support on the MCP forums.

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This comprehensive documentation outlines how to fully leverage the Example MCP Server for building robust AI applications with seamless integration capabilities. By following these guidelines, developers can enhance their application's functionality while ensuring compatibility across multiple MCP clients.