multi-mcp

Multi MCP

A flexible and dynamic Multi-MCP Proxy Server that acts as a single MCP server while connecting to and routing between

multiple backend MCP servers over `STDIO` or `SSE`.

🚀 Features

• ✅ Supports both `STDIO` and `SSE` transports
• ✅ Can connect to MCP servers running in either `STDIO` or `SSE` mode
• ✅ Proxies requests to multiple MCP servers
• ✅ Automatically initializes capabilities (tools, prompts, resources) from connected servers
• ✅ Dynamically add/remove MCP servers at runtime (via HTTP API)
• ✅ Supports tools with the same name on different servers (using namespacing)
• ✅ Deployable on Kubernetes, exposing a single port to access all connected MCP servers through the proxy

📦 Installation

To get started with this project locally:

# Clone the repository
git clone https://github.com/kfirtoledo/multi-mcp.git
cd multi-mcp

# Install using uv (recommended)
uv venv
uv pip install -r requirements.txt

🖥️ Running Locally

You can run the proxy locally in either `STDIO` or `SSE` mode depending on your needs:

1. STDIO Mode

For CLI-style operation (pipe-based communication).

Used for chaining locally executed tools or agents.

uv run main.py --transport stdio

2. SSE Mode

Runs an HTTP SSE server that exposes a `/sse` endpoint.

Useful for remote access, browser agents, and network-based tools.

uv run main.py --transport sse

Note: You can also configure the host and port using `--host` / `--port` arguments.

⚙️ Configuration

The proxy is initialized using a JSON config (default: `./mcp.json`):

{
  "mcpServers": {
    "weather": {
      "command": "python",
      "args": ["./tools/get_weather.py"]
    },
    "calculator": {
      "command": "python",
      "args": ["./tools/calculator.py"]
    }
  }
}

This config defines the initial list of MCP-compatible servers to spawn and connect at startup.

Note: Tool names are namespaced internally as `server_name::tool_name` to avoid conflicts and allow multiple servers to expose tools with the same base name. For example, if an MCP server named `calculator` provides an `add` tool, it will be referenced as `calculator::add`.

You can also connect to a remote MCP server using SSE:

{
  "mcpServers": {
    "weather": {
      "url": "http://127.0.0.1:9080/sse"
    }
  }
}

More examples can be found in the examples/config/ directory.

🔄 Dynamic Server Management (SSE only)

When running in SSE mode, you can add/remove/list MCP servers at runtime via HTTP endpoints:

Example to add a new server:

curl -X POST http://localhost:8080/mcp_servers \
  -H "Content-Type: application/json" \
  --data @add_server.json

add_server.json:

{
  "mcpServers": {
    "unit_converter": {
      "command": "python",
      "args": ["./tools/unit_converter.py"]
    }
  }
}

🐳 Docker

You can containerize and run the SSE server in K8s:

# Build the image
make docker-build

# Run locally with port exposure
make docker-run

Kubernetes

You can deploy the proxy in a Kubernetes cluster using the provided manifests.

Run with Kind

To run the proxy locally using Kind:

kind create cluster --name multi-mcp-test
kind load docker-image multi-mcp --name multi-mcp-test
kubectl apply -f k8s/multi-mcp.yaml

Exposing the Proxy

The K8s manifest exposes the SSE server via a NodePort (30080 by default):

You can then connect to the SSE endpoint from outside the cluster:

http://:30080/sse

Connecting to MCP Clients

Once the proxy is running, you can connect to it using any MCP-compatible client — such as a LangGraph agent or custom MCP client.

For example, using the langchain_mcp_adapters client, you can integrate directly with LangGraph to access tools from one or more backend MCP servers.

See `examples/connect_langgraph_client.py` for a working integration example.

Make sure your environment is set up with:

• An MCP-compatible client (e.g. LangGraph)

• .env file containing:

MODEL_NAME=
BASE_URL=
OPENAI_API_KEY=

Inspiration

This project is inspired by and builds on ideas from two excellent open-source MCP projects:

• `mcp-proxy` by @sparfenyuk
• `fastmcp` by @jlowin