---
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.

<Tip>
Clients are lightweight objects, so don't hesitate to create new ones as needed. However, be mindful of the context management - each time you open a client context (`async with client:`), a new connection or process starts. For best performance, keep client contexts open while performing multiple operations rather than repeatedly opening and closing them.
</Tip>

## Choosing a Transport

Choose the transport that best fits your use case:

- **Connecting to Remote/Persistent Servers:** Use `StreamableHttpTransport` (recommended, default for HTTP URLs) or `SSETransport` (legacy option) for web-based deployments.

- **Local Development/Testing:** Use `FastMCPTransport` for in-memory, same-process testing of your FastMCP servers.

- **Running Local Servers:** Use `UvxStdioTransport` (Python/uv) or `NpxStdioTransport` (Node/npm) if you need to run MCP servers as packaged tools.

## Network Transports

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

### Streamable HTTP

<VersionBadge version="2.3.0" />

Streamable HTTP is the recommended transport for web-based deployments, providing efficient bidirectional communication over HTTP.

#### Overview

- **Class:** `fastmcp.client.transports.StreamableHttpTransport`
- **Inferred From:** URLs starting with `http://` or `https://` (default for HTTP URLs since v2.3.0)
- **Server Compatibility:** Works with FastMCP servers running in `streamable-http` mode

#### Basic Usage

The simplest way to use Streamable HTTP is to let the transport be inferred from a URL:

```python
from fastmcp import Client
import asyncio

# The Client automatically uses StreamableHttpTransport for HTTP URLs
client = Client("https://example.com/mcp")

async def main():
    async with client:
        tools = await client.list_tools()
        print(f"Available tools: {tools}")

asyncio.run(main())
```

#### Authentication with Headers

For servers requiring authentication:

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

# Create transport with authentication headers
transport = StreamableHttpTransport(
    url="https://example.com/mcp",
    headers={"Authorization": "Bearer your-token-here"}
)

client = Client(transport)
```

### SSE (Server-Sent Events)

<VersionBadge version="2.0.0" />

Server-Sent Events (SSE) is a transport that allows servers to push data to clients over HTTP connections. While still supported, Streamable HTTP is now the recommended transport for new web-based deployments.

#### Overview

- **Class:** `fastmcp.client.transports.SSETransport`
- **Inferred From:** Not automatically inferred for HTTP URLs since v2.3.0 (must be explicitly specified)
- **Server Compatibility:** Works with FastMCP servers running in `sse` mode

#### Basic Usage

Since v2.3.0, you must explicitly create an `SSETransport` for SSE connections:

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

# Create an SSE transport
transport = SSETransport(url="https://example.com/sse")

# Pass the transport to the client
client = Client(transport)

async def main():
    async with client:
        tools = await client.list_tools()
        print(f"Available tools: {tools}")

asyncio.run(main())
```

#### Authentication with Headers

SSE transport also supports custom headers for authentication:

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

# Create SSE transport with authentication headers
transport = SSETransport(
    url="https://example.com/sse",
    headers={"Authorization": "Bearer your-token-here"}
)

client = Client(transport)
```

#### When to Use SSE vs. Streamable HTTP

- **Use Streamable HTTP when:**
  - Setting up new deployments (recommended default)
  - You need bidirectional streaming
  - You're connecting to FastMCP servers running in `streamable-http` mode

- **Use SSE when:**
  - Connecting to legacy FastMCP servers running in `sse` mode
  - Working with infrastructure optimized for Server-Sent Events

## Local 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 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" # Path to your server script

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

# Option 2: Explicit transport with custom configuration
transport = PythonStdioTransport(
    script_path=server_script,
    python_cmd="/usr/bin/python3.11", # Optional: specify Python interpreter
    # args=["--some-server-arg"],      # Optional: pass arguments to the script
    # env={"MY_VAR": "value"},         # Optional: set environment variables
)
client = Client(transport)

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

asyncio.run(main())
```

<Warning>
The server script 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" # Path to your Node.js server script

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

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

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

asyncio.run(main())
```

### UVX Stdio (Experimental)

- **Class:** `fastmcp.client.transports.UvxStdioTransport`
- **Inferred From:** Not automatically inferred
- **Use Case:** Running an MCP server packaged as a Python tool using [`uvx`](https://docs.astral.sh/uv/reference/cli/#uvx)

This is useful for executing MCP servers distributed as command-line tools or packages without installing them into your environment.

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

# Run a hypothetical 'cloud-analyzer-mcp' tool via uvx
transport = UvxStdioTransport(
    tool_name="cloud-analyzer-mcp",
    # from_package="cloud-analyzer-cli", # Optional: specify package if tool name differs
    # with_packages=["boto3", "requests"] # Optional: add dependencies
)
client = Client(transport)

async def main():
    async with client:
        result = await client.call_tool("analyze_bucket", {"name": "my-data"})
        print(f"Analysis result: {result}")

asyncio.run(main())
```

### NPX Stdio (Experimental)

- **Class:** `fastmcp.client.transports.NpxStdioTransport`
- **Inferred From:** Not automatically inferred
- **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 import Client
from fastmcp.client.transports import NpxStdioTransport

# Run an MCP server from an NPM package
transport = NpxStdioTransport(
    package="mcp-server-package",
    # args=["--port", "stdio"] # Optional: pass arguments to the package
)
client = Client(transport)

async def main():
    async with client:
        result = await client.call_tool("get_npm_data", {})
        print(f"Result: {result}")

asyncio.run(main())
```

## 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 in the same Python process

This is extremely useful for testing your FastMCP servers.

```python
from fastmcp import FastMCP, Client
import asyncio

# 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
client = Client(server)  # Transport is automatically inferred

async def main():
    async with client:
        result = await client.call_tool("ping")
        print(f"In-memory call result: {result}")

asyncio.run(main())
```

Communication happens through efficient in-memory queues, making it very fast and ideal for unit testing. 