fastmcp/docs/clients/transports.mdx

---
title: Client Transports
sidebarTitle: Transports
description: Understand the different ways FastMCP Clients can connect to servers.
icon: link
---

import { VersionBadge } from "/snippets/version-badge.mdx"

<VersionBadge version="2.0.0" />

The FastMCP `Client` relies on a `ClientTransport` object to handle the specifics of connecting to and communicating with an MCP server. FastMCP provides several built-in transport implementations for common connection methods.

While the `Client` often infers the correct transport automatically (see [Client Overview](/clients/client#transport-inference)), you can also instantiate transports explicitly for more control.


## Stdio Transports

These transports manage an MCP server running as a subprocess, communicating with it via standard input (stdin) and standard output (stdout). This is the standard mechanism used by clients like Claude Desktop.

### Python Stdio

*   **Class:** `fastmcp.client.transports.PythonStdioTransport`
*   **Inferred From:** Paths to `.py` files.
*   **Use Case:** Running a Python-based MCP server script (like one using FastMCP or the base `mcp` library) in a subprocess.

This is the most common way to interact with local FastMCP servers during development or when integrating with tools that expect to launch a server script.

```python
from fastmcp import Client
from fastmcp.client.transports import PythonStdioTransport

server_script = "my_mcp_server.py" # Assumes this file exists and runs mcp.run()

# Option 1: Inferred transport
client_inferred = Client(server_script)

# Option 2: Explicit transport (e.g., to use a specific python executable or add args)
transport_explicit = PythonStdioTransport(
    script_path=server_script,
    python_cmd="/usr/bin/python3.11", # Specify python version
    # args=["--some-server-arg"], # Pass args to the script
    # env={"MY_VAR": "value"},   # Set environment variables
    # cwd="/path/to/run/in"       # Set working directory
)
client_explicit = Client(transport_explicit)

async def use_stdio_client(client):
    async with client:
        tools = await client.list_tools()
        print(f"Connected via Python Stdio, found tools: {tools}")

# asyncio.run(use_stdio_client(client_inferred))
# asyncio.run(use_stdio_client(client_explicit))
```

<Warning>
The server script (`my_mcp_server.py` in the example) *must* include logic to start the MCP server and listen on stdio, typically via `mcp.run()` or `fastmcp.server.run()`. The `Client` only launches the script; it doesn't inject the server logic.
</Warning>

### Node.js Stdio

*   **Class:** `fastmcp.client.transports.NodeStdioTransport`
*   **Inferred From:** Paths to `.js` files.
*   **Use Case:** Running a Node.js-based MCP server script in a subprocess.

Similar to the Python transport, but for JavaScript servers.

```python
from fastmcp import Client
from fastmcp.client.transports import NodeStdioTransport

node_server_script = "my_mcp_server.js" # Assumes this JS file starts an MCP server on stdio

# Option 1: Inferred transport
client_inferred = Client(node_server_script)

# Option 2: Explicit transport
transport_explicit = NodeStdioTransport(
    script_path=node_server_script,
    node_cmd="node" # Or specify path to Node executable
)
client_explicit = Client(transport_explicit)

# Usage is the same as other clients
# async with client_explicit:
#    tools = await client_explicit.list_tools()
```

### UVX Stdio (Experimental)

*   **Class:** `fastmcp.client.transports.UvxStdioTransport`
*   **Inferred From:** Not automatically inferred. Must be instantiated explicitly.
*   **Use Case:** Running an MCP server packaged as a Python tool using [`uvx`](https://docs.astral.sh/uv/reference/cli/#uvx) (part of the `uv` toolchain). This allows running tools without explicitly installing them into the current environment.

This is useful for executing MCP servers distributed as command-line tools or packages.

```python
from fastmcp.client.transports import UvxStdioTransport

# Example: Run a hypothetical 'cloud-analyzer-mcp' tool via uvx
# Assume this tool, when run, starts an MCP server on stdio
transport = UvxStdioTransport(
    tool_name="cloud-analyzer-mcp",
    # from_package="cloud-analyzer-cli", # Optionally specify package if tool name differs
    # with_packages=["boto3", "requests"], # Add dependencies if needed
    # tool_args=["--config", "prod.yaml"] # Pass args to the tool itself
)
client = Client(transport)

# async with client:
#     analysis = await client.call_tool("analyze_bucket", {"name": "my-data"})
```

### NPX Stdio (Experimental)

*   **Class:** `fastmcp.client.transports.NpxStdioTransport`
*   **Inferred From:** Not automatically inferred. Must be instantiated explicitly.
*   **Use Case:** Running an MCP server packaged as an NPM package using `npx`.

Similar to `UvxStdioTransport`, but for the Node.js ecosystem.

```python
from fastmcp.client.transports import NpxStdioTransport

# Example: Run a hypothetical 'npm-mcp-server-package' via npx
transport = NpxStdioTransport(
    package="npm-mcp-server-package",
    # args=["--port", "stdio"] # Args passed to the package script
)
client = Client(transport)

# async with client:
#     response = await client.call_tool("get_npm_data", {})
```
## Network Transports

These transports connect to servers running over a network, typically long-running services accessible via URLs.

### SSE (Server-Sent Events)

*   **Class:** `fastmcp.client.transports.SSETransport`
*   **Inferred From:** `http://` or `https://` URLs
*   **Use Case:** Connecting to persistent MCP servers exposed over HTTP/S, often using FastMCP's `mcp.run(transport="sse")` mode.

SSE is a simple, unidirectional protocol where the server pushes messages to the client over a standard HTTP connection.

```python
from fastmcp import Client
from fastmcp.client.transports import SSETransport

sse_url = "http://localhost:8000/sse"

# Option 1: Inferred transport
client_inferred = Client(sse_url)

# Option 2: Explicit transport (e.g., to add custom headers)
headers = {"Authorization": "Bearer mytoken"}
transport_explicit = SSETransport(url=sse_url, headers=headers)
client_explicit = Client(transport_explicit)

async def use_sse_client(client):
    async with client:
        tools = await client.list_tools()
        print(f"Connected via SSE, found tools: {tools}")

# asyncio.run(use_sse_client(client_inferred))
# asyncio.run(use_sse_client(client_explicit))
```

### WebSocket

*   **Class:** `fastmcp.client.transports.WSTransport`
*   **Inferred From:** `ws://` or `wss://` URLs
*   **Use Case:** Connecting to MCP servers using the WebSocket protocol for bidirectional communication.

WebSockets provide a persistent, full-duplex connection between client and server.

```python
from fastmcp import Client
from fastmcp.client.transports import WSTransport

ws_url = "ws://localhost:9000"

# Option 1: Inferred transport
client_inferred = Client(ws_url)

# Option 2: Explicit transport
transport_explicit = WSTransport(url=ws_url)
client_explicit = Client(transport_explicit)

async def use_ws_client(client):
    async with client:
        tools = await client.list_tools()
        print(f"Connected via WebSocket, found tools: {tools}")

# asyncio.run(use_ws_client(client_inferred))
# asyncio.run(use_ws_client(client_explicit))
```

## In-Memory Transports

### FastMCP Transport

*   **Class:** `fastmcp.client.transports.FastMCPTransport`
*   **Inferred From:** An instance of `fastmcp.server.FastMCP`.
*   **Use Case:** Connecting directly to a `FastMCP` server instance running in the *same Python process*.

This is extremely useful for:
*   **Testing:** Writing unit or integration tests for your FastMCP server without needing subprocesses or network connections.
*   **Embedding:** Using an MCP server as a component within a larger application.

```python
from fastmcp import FastMCP, Client
from fastmcp.client.transports import FastMCPTransport

# 1. Create your FastMCP server instance
server = FastMCP(name="InMemoryServer")
@server.tool()
def ping(): return "pong"

# 2. Create a client pointing directly to the server instance
# Option A: Inferred
client_inferred = Client(server)

# Option B: Explicit
transport_explicit = FastMCPTransport(mcp=server)
client_explicit = Client(transport_explicit)

# 3. Use the client (no subprocess or network involved)
async def test_in_memory():
    async with client_inferred: # Or client_explicit
        result = await client_inferred.call_tool("ping")
        print(f"In-memory call result: {result[0].text}") # Output: pong

# asyncio.run(test_in_memory())
```
Communication happens through efficient in-memory queues, making it very fast.

## Choosing a Transport

*   **Local Development/Testing:** Use `PythonStdioTransport` (inferred from `.py` files) or `FastMCPTransport` (for same-process testing).
*   **Connecting to Remote/Persistent Servers:** Use `SSETransport` (for `http/s`) or `WSTransport` (for `ws/s`).
*   **Running Packaged Tools:** Use `UvxStdioTransport` (Python/uv) or `NpxStdioTransport` (Node/npm) if you need to run MCP servers without local installation.
*   **Integrating with Claude Desktop (or similar):** These tools typically expect to run a Python script, so your server should be runnable via `python your_server.py`, making `PythonStdioTransport` the relevant mechanism on the client side.