Part of GPT‑RAG

The GPT-RAG MCP service deploys a Python MCP server built with FastMCP and Starlette. It is consumed by GPT-RAG through the orchestrator mcp strategy and is not tied to an AutoGen-specific runtime. Documentation on the Model Context Protocol can be found here: Model Context Protocol

Before deploying the application, you must provision the infrastructure as described in the GPT-RAG repo. This includes creating all necessary Azure resources required to support the application runtime.

• Ensure the .azure directory is present in the root
• azd deploy

To deploy using a script, first clone the repository, set the App Configuration endpoint, and then run the deployment script.

$env:APP_CONFIG_ENDPOINT = "https://<your-app-config-name>.azconfig.io"
cd gpt-rag-mcp
.\scripts\deploy.ps1

• Navigate to App Configuration resource in Azure portal
• Update AGENT_STRATEGY variable to mcp
• Update MCP_SERVER_URL variable to <container_app_url>/mcp (e.g. https://{container-app-name}.{container-app-region}.azurecontainerapps.io/mcp) or the desired MCP Server URL (if external)
• In Azure Portal, Stop Orchestrator Container App, then restart it
• Open frontend URL in your browser and ask "What tools are available to you?" If the MCP Server is working properly, it will list tools and their functionality.

• Run the following command in bash or pwsh

npx @modelcontextprotocol/inspector

• Click on the link displayed in terminal that says "MCP Inspector is up and running at..."
• Plug in your container Application URL (found on container app overview page in Azure portal) followed by /mcp (e.g. https://<container_app_name>.eastus.azurecontainerapps.io/mcp)

• Create a directory .vscode in your root
• Move launch.json into .vscode
• Update the app configuration resource
• Run VS Code Debugger