tRPC <-> MCP Server: Seamless Integration of tRPC Routes into Model Context Protocol (MCP)

Overview: What is tRPC <-> MCP?

tRPC <-> MCP serves as a bridge between the powerful tRPC framework and the versatile MCP protocol, enabling developers to easily integrate AI applications with specific data sources and tools through a standardized interface. This server acts as an adapter, facilitating seamless communication between AI tools like Claude Desktop, Continue, Cursor, and others.

🔧 Core Features & MCP Capabilities

The tRPC <-> MCP server is designed to provide core features that align with the Model Context Protocol (MCP). These capabilities include enabling tRPC procedures via MCP, ensuring compatibility across various clients, and optimizing the flow of data through a standardized protocol. By using this server, developers can leverage existing tRPC schema definitions to create robust APIs that meet MCP requirements.

⚙️ MCP Architecture & Protocol Implementation

Client Compatibility with MCP

The tRPC <-> MCP server supports a range of compatible MCP clients, including:

• Claude Desktop: Full support for data retrieval and tool integration.
• Continue: Comprehensive compatibility supporting all features.
• Cursor: Limited to tools only due to certain constraints.

MCP ensures that these applications can efficiently interact with external services and data sources. Developers can easily integrate different tools and resources into their applications, enhancing functionality and improving user experience.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph TD
    A[API Endpoint] --> B[tRPC Router]
    B --> C[MCP Metadata]
    C --> D[MCP Server]
    D --> E[Data Source/Tool]
    style A fill:#d1f3ff
    style C fill:#fff5e8
    style D fill:#e8f5e8

🚀 Getting Started with Installation

To get started, follow these steps:

1. Add MCP Metadata to tRPC Schema:

   import { initTRPC } from '@trpc/server';
   import { type McpMeta } from 'trpc-to-openapi';
   
   const t = initTRPC.meta<McpMeta>().create();
   

2. Enable MCP for Specific Routes:

   export const appRouter = t.router({
     sayHello: t.procedure
       .meta({ openapi: { enabled: true, description: 'Greet the user' } })
       .input(z.object({ name: z.string() }))
       .output(z.object({ greeting: z.string() }))
       .query(({ input }) => {
         return { greeting: `Hello ${input.name}!` };
       });
   });
   

3. Create and Serve the MCP Server:

   import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
   import { createMcpServer } from 'trpc-mcp';
   
   const mcpServer = createMcpServer(
     { name: 'trpc-mcp-example', version: '0.0.1' },
     appRouter,
   );
   
   const transport = new StdioServerTransport();
   await mcpServer.connect(transport);
   

💡 Key Use Cases in AI Workflows

Use Case 1: Integration with Data Sources

Consider an example where your application needs to fetch user data from a database. Using tRPC <-> MCP, this process is streamlined:

1. Define the Data Fetch Procedure:

   export const appRouter = t.router({
     fetchData: t.procedure
       .meta({ openapi: { enabled: true, description: 'Fetch user data' } })
       .input(z.object({ userId: z.string() }))
       .output(z.object({ user: z.object({ id: z.string(), name: z.string() }) }))
       .query(({ input }) => {
         // Logic to fetch user data from the database
         return { user: { id: '123', name: 'John Doe' } };
       });
   });
   

2. Serve the Procedure via MCP:

   const mcpServer = createMcpServer(
     { name: 'trpc-mcp-example', version: '0.0.1' },
     appRouter,
   );
   
   const transport = new StdioServerTransport();
   await mcpServer.connect(transport);
   

Use Case 2: Tool Integration

Suppose your application needs to interact with an external tool, such as a language model API:

1. Define the API Endpoints:

   export const appRouter = t.router({
     getResponse: t.procedure
       .meta({ openapi: { enabled: true, description: 'Get response from external tool' } })
       .input(z.object({ prompt: z.string() }))
       .output(z.object({ response: z.string() }))
       .query(({ input }) => {
         // Logic to send the prompt and get a response
         return { response: "Here is the generated text." };
       });
   });
   

2. Serve the API via MCP:

   const mcpServer = createMcpServer(
     { name: 'trpc-mcp-example', version: '0.0.1' },
     appRouter,
   );
   
   const transport = new StdioServerTransport();
   await mcpServer.connect(transport);
   

🔌 Integration with MCP Clients

The tRPC <-> MCP server ensures compatibility with a range of MCP clients, supporting full integration for applications like Claude Desktop and Continue. It provides a seamless way to connect AI tools through the MCP protocol, allowing for efficient data exchange and robust API capabilities.

📊 Performance & Compatibility Matrix

graph LR
    A[AI Application] -->|MCP Client| B{Compatible Clients}
    B --> C[Claude Desktop]
    B --> D[Continue]
    B --> E[Cursor]

Compatibility Matrix for MCP Clients

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

🛠️ Advanced Configuration & Security

The tRPC <-> MCP server can be configured to enhance security and performance. The following configuration sample provides an example of how to set up the environment:

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

This configuration ensures that the server is properly secured and optimized for production environments.

❓ Frequently Asked Questions (FAQ)

1. Q: How does tRPC <-> MCP ensure compatibility with different AI applications? A: The server supports a wide range of clients, including Claude Desktop, Continue, and Cursor. Each client has specific capabilities; for instance, Continue offers full support across all features.

2. Q: Can I use this server to integrate external tools with my application? A: Yes, the tRPC <-> MCP server allows you to define API endpoints that can interact with external tools seamlessly through the MCP protocol.

3. Q: How does the data flow work in the tRPC <-> MCP configuration? A: The data flow starts from an AI application or client, which sends requests via the MCP Protocol. These requests are then processed by the server and may involve fetching data from a database or interacting with an external tool before returning results to the client.

4. Q: Is there any performance overhead when using this integration? A: The implementation is designed to minimize performance overhead, particularly in high-traffic scenarios. Proper configuration can significantly enhance performance and reduce latency.

5. Q: Can I customize the MCP metadata for specific procedures? A: Absolutely. You can add detailed metadata for each procedure via the meta function, providing descriptions, input/output types, etc., that are critical for compatibility with different clients.

👨‍💻 Development & Contribution Guidelines

Contributions to the tRPC <-> MCP project are encouraged and greatly appreciated. If you wish to contribute, please follow these guidelines:

1. Fork the Repository: Start by forking the official repository.
2. Create a New Branch: Create a new branch from the main branch or the latest development version.
3. Make Your Changes: Implement your changes according to best practices and coding standards.
4. Run Tests: Ensure all tests pass before submitting your pull request.
5. Submit a Pull Request: Clearly describe the changes you have made in the pull request.

🌐 MCP Ecosystem & Resources

Stay up-to-date with the latest developments in the Model Context Protocol ecosystem by visiting the official documentation and community resources:

• ModelContextProtocol.org
• Join the discussion in the official forums: MCP Community Forum

By leveraging these resources, you can gain deeper insights into MCP and its applications, ensuring that your integration efforts are as effective as possible.

---

This comprehensive documentation positions the tRPC <-> MCP server as a vital tool for integrating tRPC routes with Model Context Protocol clients, enhancing AI workflows and application capabilities.