iflow-mcp_yahelb05-mcp-proxy

# MCProxy - MCP Proxy for RESTful APIs MCProxy easley and almost seamlessly generates and deploys a proxy between your <b>existing</b> API and the clients. <br /> It won't replace the existing interface with the existing API, but it will extend the functionality of your system with <b>MCP</b>. <br /> --- ## What's MCP? MCP (Model Context Protocol) is a standardized communication protocol that enables AI tools and services to interact seamlessly, where servers expose operations (“tools”) with defined input/output schemas and clients can discover, invoke, and use them dynamically. <br /> In this ecosystem, an MCP Server acts as a gateway, exposing AI capabilities, workflows, or APIs—often with streaming outputs—while an MCP Client, typically an AI agent or application, discovers and calls these tools to perform tasks. <br /> <br /> <b>MCProxy</b> acts as an MCP Server proxy layer that wraps your existing REST API, enabling AI clients to interact with your services using the MCP protocol without changing your underlying API. --- ## Deployment Deploying **MCProxy** is straightforward: 1. **Build the Docker image:** `docker build -t mcp-proxy:<version> .` 2. **Prepare the configuration file** Define the endpoints you want to expose to MCP clients. By default, MCProxy will look for `config.json`. 3. **Mount the configuration file** Mount it into your Docker container, Kubernetes pod, or deployment. 4. **Expose MCProxy** Make the service accessible via your preferred routing or ingress method. 💡 You can check the examples in `./dev/` for inspiration on configuration and setup. Once deployed, MCP clients can connect to MCProxy and access exactly the data you’ve allowed — **no extra coding required**. --- ## Environment Variables | Variable | Description | Default | |---------------|--------------------------------------|-----------------| | `CONFIG_FILE` | Full path to the configuration file. | `./c…