---
title: Client Transports
sidebarTitle: Transports
description: Configure how clients connect to and communicate with MCP servers.
icon: link
---

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

<VersionBadge version="2.0.0" />

Transports handle the underlying connection between your client and MCP servers. While the client can automatically select a transport based on what you pass to it, instantiating transports explicitly gives you full control over configuration.

## STDIO Transport

STDIO transport communicates with MCP servers through subprocess pipes. When using STDIO, your client launches and manages the server process, controlling its lifecycle and environment.

<Warning>
STDIO servers run in isolated environments by default. They do not inherit your shell's environment variables. You must explicitly pass any configuration the server needs.
</Warning>

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

transport = StdioTransport(
    command="python",
    args=["my_server.py", "--verbose"],
    env={"API_KEY": "secret", "LOG_LEVEL": "DEBUG"},
    cwd="/path/to/server"
)
client = Client(transport)
```

For convenience, the client can infer STDIO transport from file paths, though this limits configuration options:

```python
from fastmcp import Client

client = Client("my_server.py")  # Limited - no configuration options
```

### Environment Variables

Since STDIO servers do not inherit your environment, you need strategies for passing configuration.

**Selective forwarding** passes only the variables your server needs:

```python
import os
from fastmcp.client.transports import StdioTransport

required_vars = ["API_KEY", "DATABASE_URL", "REDIS_HOST"]
env = {var: os.environ[var] for var in required_vars if var in os.environ}

transport = StdioTransport(command="python", args=["server.py"], env=env)
client = Client(transport)
```

**Loading from .env files** keeps configuration separate from code:

```python
from dotenv import dotenv_values
from fastmcp.client.transports import StdioTransport

env = dotenv_values(".env")
transport = StdioTransport(command="python", args=["server.py"], env=env)
client = Client(transport)
```

### Session Persistence

STDIO transports maintain sessions across multiple client contexts by default (`keep_alive=True`). This reuses the same subprocess for multiple connections, improving performance.

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

transport = StdioTransport(command="python", args=["server.py"])
client = Client(transport)

async def efficient_multiple_operations():
    async with client:
        await client.ping()

    async with client:  # Reuses the same subprocess
        await client.call_tool("process_data", {"file": "data.csv"})
```

For complete isolation between connections, disable session persistence:

```python
transport = StdioTransport(command="python", args=["server.py"], keep_alive=False)
```

## HTTP Transport

<VersionBadge version="2.3.0" />

HTTP transport connects to MCP servers running as web services. This is the recommended transport for production deployments.

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

transport = StreamableHttpTransport(
    url="https://api.example.com/mcp",
    headers={
        "Authorization": "Bearer your-token-here",
        "X-Custom-Header": "value"
    }
)
client = Client(transport)
```

FastMCP also provides authentication helpers:

```python
from fastmcp import Client
from fastmcp.client.auth import BearerAuth

client = Client(
    "https://api.example.com/mcp",
    auth=BearerAuth("your-token-here")
)
```

### SSE Transport

Server-Sent Events transport is maintained for backward compatibility. Use Streamable HTTP for new deployments unless you have specific infrastructure requirements.

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

transport = SSETransport(
    url="https://api.example.com/sse",
    headers={"Authorization": "Bearer token"}
)
client = Client(transport)
```

## In-Memory Transport

In-memory transport connects directly to a FastMCP server instance within the same Python process. This eliminates both subprocess management and network overhead, making it ideal for testing.

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

mcp = FastMCP("TestServer")

@mcp.tool
def greet(name: str) -> str:
    prefix = os.environ.get("GREETING_PREFIX", "Hello")
    return f"{prefix}, {name}!"

client = Client(mcp)

async with client:
    result = await client.call_tool("greet", {"name": "World"})
```

<Note>
Unlike STDIO transports, in-memory servers share the same memory space and environment variables as your client code.
</Note>

## Multi-Server Configuration

<VersionBadge version="2.4.0" />

Connect to multiple servers defined in a configuration dictionary:

```python
from fastmcp import Client

config = {
    "mcpServers": {
        "weather": {
            "url": "https://weather.example.com/mcp",
            "transport": "http"
        },
        "assistant": {
            "command": "python",
            "args": ["./assistant.py"],
            "env": {"LOG_LEVEL": "INFO"}
        }
    }
}

client = Client(config)

async with client:
    # Tools are namespaced by server
    weather = await client.call_tool("weather_get_forecast", {"city": "NYC"})
    answer = await client.call_tool("assistant_ask", {"question": "What?"})
```

### Tool Transformations

FastMCP supports tool transformations within the configuration. You can change names, descriptions, tags, and arguments for tools from a server.

```python
config = {
    "mcpServers": {
        "weather": {
            "url": "https://weather.example.com/mcp",
            "transport": "http",
            "tools": {
                "weather_get_forecast": {
                    "name": "miami_weather",
                    "description": "Get the weather for Miami",
                    "arguments": {
                        "city": {
                            "default": "Miami",
                            "hide": True,
                        }
                    }
                }
            }
        }
    }
}
```

To filter tools by tag, use `include_tags` or `exclude_tags` at the server level:

```python
config = {
    "mcpServers": {
        "weather": {
            "url": "https://weather.example.com/mcp",
            "include_tags": ["forecast"]  # Only tools with this tag
        }
    }
}
```
