MCP Bridge: Connecting Cloud AI Services to Local MCP Servers

Overview: What is MCP Bridge?

MCP (Model Context Protocol) introduced by Anthropic is an industry-standard protocol that allows AI applications to interact with specific data sources and tools through a unified interface, enhancing their flexibility and interoperability. However, most MCP servers are built on Stdio transport, which, while excellent for accessing local resources, limits their use in cloud-based applications.

MCP bridge is a lightweight tool designed to solve this problem by enabling cloud-based AI services to interact with local MCP servers using HTTP/HTTPS protocols. It acts as a translator between these communication methods, ensuring the seamless integration of MCP servers into broader cloud environments while maintaining high security standards.

🔧 Core Features & MCP Capabilities

MCP bridge offers several core features and capabilities that make it an indispensable tool for integrating AI applications with local resources:

Cloud Integration: It seamlessly bridges the gap between cloud-based AI services and local Stdio-based MCP servers, allowing them to communicate via HTTP/HTTPS.
Protocol Translation: Converts network requests into Stdio communication, ensuring efficient data transfer without altering the underlying implementation of the MCP server.
Security: Provides secure access to local resources while maintaining control over authentication and authorization mechanisms. This is crucial for protecting sensitive information during cloud integrations.
Flexibility: Supports a wide range of different MCP servers without requiring any modifications to their existing codebase, thus promoting ease of use and broader adoption.
Ease of Use: Designed with simplicity in mind; users can run the bridge and the MCP server with minimal configuration changes required on either side.

⚙️ Architecture & Protocol Implementation

The architecture of MCP bridge is designed around a small number of key components:

HTTP/HTTPS Servers: These servers handle incoming HTTP requests from cloud-based services, converting them into Stdio communications.
Proxy Services: Proxy the communication between the cloud-based AI tools and the local MCP server, ensuring that all data transfers are handled securely.

The protocol implementation follows the standard Model Context Protocol (MCP) specifications but extends support beyond the traditional Stdio transport to HTTP/HTTPS. This integration ensures that developers can seamlessly connect various AI applications with local resources using familiar and secure communication protocols like HTTPS.

🚀 Getting Started with Installation

Prerequisites

Node.js: Ensure you have a recent version of Node.js installed on your development machine.

Quick Start Guide

Clone the Repository

git clone https://github.com/modelcontextprotocol/mcp-bridge.git

Enter the Directory
```
cd mcp-bridge
```
Copy Environment Variables Example File
```
cp .env.example .env
```
Configure Environments Variables Edit .env to set necessary environment variables such as port and authentication tokens.
Install Dependencies
```
npm install
```
Run the Bridge
- For production use:
```
npm run start:tunnel
```
- In development mode (supports hot-reloading):
```
npm run dev:tunnel
```

Now, MCP bridge should be running on http://localhost:3000/bridge.

💡 Key Use Cases in AI Workflows

Use Case 1: Remote Code Execution

Imagine a scenario where an AI desktop application needs to fetch and analyze data from various repositories hosted on GitHub. With the help of MCP Bridge, developers can easily configure an MCP server to handle such requests securely over HTTP.

Use Case 2: Data Analysis on Demand

Consider a real-time monitoring system that requires access to multiple local databases for continuous analysis. By integrating MCP bridge, this system can connect to these databases using HTTP/HTTPS, ensuring low latency and high performance without compromising security.

🔌 Integration with MCP Clients

MCP Bridge is compatible with several popular MCP clients including Claude Desktop, Continue, and Cursor. The following table provides an overview of compatibility:

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

📊 Performance & Compatibility Matrix

The performance of MCP bridge in different scenarios has been thoroughly tested and documented. The compatibility matrix below offers an overview:

Use Cases

GitHub API Integration: Seamless interaction with GitHub services.
Database Access: Secure access to local databases using HTTP endpoints.

🛠️ Advanced Configuration & Security

Example Configuration Code Sample

Below is a sample configuration for integrating MCP Bridge with the official MCP server for GitHub:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "<your_github_personal_access_token>"
      }
    }
  }
}

Security Best Practices

Token-Based Authentication: Use a robust authentication mechanism to ensure secure communication.
Authorization Levels: Implement different levels of access control based on user roles.

❓ Frequently Asked Questions (FAQ)

What if my AI client does not support HTTP protocols? MCP bridge supports both HTTPS and SSE for robust cloud integration, ensuring compatibility with a wide range of clients.
Can I configure multiple servers in one environment? Yes, MCP Bridge can manage multiple servers within the same configuration file. Each server can be configured separately to handle different tasks.
How do I handle large datasets using this bridge? The bridge supports streaming data and handles large payloads efficiently, making it suitable for real-time data processing applications.
Is there a limit on concurrent connections? MCP Bridge is designed to handle multiple concurrent connections without significant performance degradation. For detailed limits, refer to the official documentation.
How do I ensure data privacy during cloud integration? Implement encryption and authentication mechanisms to secure all data transfers, ensuring compliance with data protection regulations.

👨‍💻 Development & Contribution Guidelines

If you're interested in contributing to MCP Bridge or enhancing its features, follow these steps:

Fork the Repository: Go to GitHub and fork the mcp-bridge repository.
Clone Your Fork: Clone your fork locally using git clone <your-fork-url>.
Set Up the Project:
- Navigate into your new directory: cd mcp-bridge
- Install dependencies: npm install
Run Tests and Linter
Develop New Features or Fix Bugs: Use Git for version control.
Submit a Pull Request (PR)

🌐 MCP Ecosystem & Resources

Join the growing community of MCP experts and contributors by visiting MCP Bridge's official GitHub page, where you can find additional resources, bug reports, and feature requests:

By following these guidelines, users can effectively integrate their AI applications with local resources using the power of HTTP/HTTPS, thanks to the versatile MCP Bridge.

MCP Bridge