GPT MCP Proxy: Integrating AI Applications with Model Context Protocol

Overview: What is GPT MCP Proxy?

GPT MCP Proxy is a REST API server that provides HTTP access to Multiple Command Protocol (MCP) tools. It acts as a bridge between HTTP clients and MCP-compliant tool servers, enabling seamless integration of AI applications with various data sources and tools through standardized protocol endpoints. This server is particularly useful for integrating MCP tools with custom GPT systems via Actions, facilitating easier development and deployment in AI workflows.

🔧 Core Features & Model Context Protocol Capabilities

GPT MCP Proxy supports a range of features that make it an essential tool for developers working with MCP-compliant environments:

1. Tool Server Discovery: It allows users to list available MCP servers and their tools.
2. Tool Information: Detailed information about each tool can be obtained, enabling better understanding and usage.
3. Tool Execution: Tools can be executed using custom parameters, providing flexibility in how tasks are performed.
4. OpenAPI Specification: The server is fully compatible with OpenAPI 3.1.0 to ensure seamless integration and documentation of APIs.
5. Automatic HTTPS Exposure: The built-in support for ngrok ensures that the server can be publicly accessible over HTTPS without additional manual setup.

⚙️ MCP Architecture & Protocol Implementation

The GPT MCP Proxy implements a robust architecture based on Model Context Protocol, which is designed to standardize interactions between AI applications and data sources. Below is an illustration of the 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

This diagram shows the flow of communication between an AI application, the MCP client, the MCP server, and ultimately to a data source or tool. The server acts as the central hub, facilitating secure and efficient data exchange.

🚀 Getting Started with Installation

Getting started with GPT MCP Proxy involves several steps:

1. Prerequisites: Ensure that Go 1.20 or later is installed on your system, along with an active ngrok account and authtoken.
2. Environment Variables: Set up the necessary environment variables:

   NGROK_AUTH_TOKEN=your_ngrok_auth_token
   NGROK_DOMAIN=your_ngrok_domain
   MCP_CONFIG_FILE=/path/to/mcp_settings.json  # Optional, defaults to mcp_settings.json
   

3. Configuration File: Prepare an mcp_settings.json file with your specific configurations:

   {
     "mcpServers": {
       "filesystem": {
         "command": "npx",
         "args": [
           "-y",
           "@modelcontextprotocol/server-filesystem",
           "/Users/username/Desktop",
           "/path/to/other/allowed/dir"
         ]
       }
     }
   }
   

4. Run the Server: Execute the server using Go:

   go run main.go
   

💡 Key Use Cases in AI Workflows

GPT MCP Proxy can be integrated into numerous AI workflows, enhancing the capabilities of various AI applications by providing seamless access to a wide range of tools and data sources. Here are two realistic use cases:

Use Case 1: Document Generation with Claude Desktop

In this scenario, an AI application like Claude Desktop uses GPT MCP Proxy to communicate with a document management system. The process involves:

1. Tool Discovery: The client discovers the available MCP servers.
2. Data Access: The tool accesses files from the document management system via the MCP protocol.
3. Task Execution: The tool generates a new document based on predefined prompts and templates.

Use Case 2: Data Analysis with Cursor

This use case involves using GPT MCP Proxy to integrate an AI application like Cursor, which needs access to multiple data sources for analysis:

1. Tool Discovery: Identifying available MCP servers.
2. Data Access: Fetching data from various sources, such as databases or APIs.
3. Analysis Execution: Running complex queries and generating insights based on the fetched data.

🔌 Integration with MCP Clients

GPT MCP Proxy supports a comprehensive range of MCP clients, including popular AI applications like Claude Desktop, Continue, and Cursor. The following compatibility matrix provides an overview:

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

This matrix helps developers understand which features are supported by different clients, facilitating informed decision-making during integration.

📊 Performance & Compatibility Matrix

The performance and compatibility of GPT MCP Proxy have been tested extensively to ensure reliability and robustness. The following table provides a summary:

Feature	Status
Tool Server Discovery	✅
Tool Information Retrieval	✅
Tool Execution with Parameters	✅
OpenAPI Compatibility	✅
Automatic HTTPS Exposure via ngrok	✅

This compatibility matrix ensures that the server meets high standards for performance and reliability.

🛠️ Advanced Configuration & Security

For advanced users, GPT MCP Proxy offers a variety of configuration options to enhance security and functionality:

1. MCP Settings File: Customize the mcp_settings.json file to include specific tools and their parameters.
2. Environment Variables: Modify environment variables to adjust server behavior, such as changing the ngrok domain or authorization token.

Security is paramount, and GPT MCP Proxy provides several layers of protection:

• Authentication via ngrok: Ensures secure public access through automatic HTTPS with a valid authtoken.
• Tool Execution Control: Users can restrict which tools are accessible and how they operate to maintain data integrity and security.

❓ Frequently Asked Questions (FAQ)

1. Q: Can GPT MCP Proxy be used with any AI application?
   • A: Yes, it supports popular AI applications like Claude Desktop and Continue. Cursor is also compatible but lacks support for resource management.
2. Q: What are the supported tools in GPT MCP Proxy?
   • A: The supported tools include filesystem access, database interfaces, and data processing engines.
3. Q: How do I set up the OpenAPI specification?
   • A: You can generate the OpenAPI spec by hitting the GET /openapi.json endpoint on your server.
4. Q: Can we change the ngrok domain dynamically during runtime?
   • A: While not directly supported, you can configure dynamic changes via environment variables or through a custom script.
5. Q: How do I debug issues with tool execution?
   • A: Check the logs generated by GPT MCP Proxy and ensure that all tools are correctly registered in the mcp_settings.json file.

👨‍💻 Development & Contribution Guidelines

Contributions to GPT MCP Proxy are welcome! Developers can contribute by submitting pull requests for bug fixes, feature enhancements, or documentation updates. If you wish to contribute, please follow these steps:

1. Fork and Clone: Fork the repository and clone it locally.
2. Install Dependencies: Ensure that all dependencies are correctly set up.
3. Develop & Test: Make your changes, run tests, and ensure everything works as expected.
4. Commit & Push: Commit your changes and push them to your forked repository.
5. Create Pull Request: Create a pull request detailing the changes you made.

🌐 MCP Ecosystem & Resources

For more information about the Model Context Protocol (MCP), check out the official documentation and community resources:

• Model Context Protocol Documentation
• Community Forums

GPT MCP Proxy is part of a larger ecosystem designed to enhance AI application development through standardized protocol integration.