Used to install the uv package manager, which is recommended for setting up the MCP server environment
Allows mounting multiple MCP servers in a FastAPI application, enabling organization of different tool sets as separate endpoints
The MCP server implementation is built using Python, with support for Python version management tools like pyenv
Supports deployment on the Render platform through runtime.txt configuration
MCP Servers over Streamable HTTP — Step-by-Step Guide
📝 Read the full article here: MCP Servers over Streamable HTTP (Step-by-Step)
This repository contains a complete, working example of how to build and run an MCP (Model Context Protocol) server using Python, mcp
, and FastAPI
. You’ll learn how to:
- Expose tools and functions over HTTP using the MCP protocol
- Connect those tools to AI assistants like Cursor
- Use streamable HTTP as the transport
- Mount multiple MCP servers in a FastAPI app
📁 Folder Structure
⸻
🛠 Quickstart 1. Install uv (recommended Python package manager)
- Install dependencies and set up environment
- Run the basic MCP server This uses the Tavily API to expose a simple web_search tool.
- Run the FastAPI app with multiple MCP servers
This will mount:
⸻
🧪 Debug with MCP Inspector 1. Install CLI support
- Launch the inspector
Then go to: http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=...
⸻
🔌 Connect to Cursor
In Cursor, add your MCP server under Chat Settings > MCP Servers:
✅ Note: You must include the trailing / in the URL.
⸻
This server cannot be installed
A complete working example of Model Context Protocol servers that exposes tools and functions over streamable HTTP, allowing AI assistants like Cursor to access capabilities such as web search via Tavily.
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