---
title: "MCP Tools Server"
description: "Serving your tools as MCP (Model Context Protocol) endpoints."
icon: "server"
---

Agency Swarm supports serving your tools as production-ready MCP (Model Context Protocol) endpoints.
This enables AI models to interact with your tools remotely over HTTP, making them accessible to any MCP-compatible client or AI system.

## Setting Up MCP Server

To expose tools as a standalone MCP endpoint, use the `run_mcp()` function from the integrations module.
This will create an MCP server that will serve provided tools over the streamable HTTP protocol.

### Example: Serving Individual Tools

```python
from agency_swarm import function_tool, run_mcp
from pydantic import BaseModel, Field

class InputArgs(BaseModel):
    input_field: str = Field(..., description="Test input for the tool")

@function_tool
async def echo_function(args: InputArgs) -> str:
    """Returns a unique id"""
    return f"Tool called with input: {args.input_field}"

# Start MCP server with individual tools.
# This will setup a streamable-http server by default on port 8000
run_mcp(tools=[echo_function])
```

### Configuration Options

The `run_mcp()` function accepts several configuration parameters:

```python
run_mcp(
    tools=[ExampleTool, TestTool],  # List of Function tools or BaseTools
    host="0.0.0.0",                 # Host to bind server to
    port=8000,                      # Port to bind server to
    app_token_env="APP_TOKEN",      # Environment variable for auth token
    server_name="mcp-tools-server", # MCP server identifier
    return_app=False,               # Return app instead of running server
    transport="streamable-http"     # Preferred mcp protocol to use
)
```

### Authentication

Authentication is controlled via environment variables:

```python
import os
os.environ["APP_TOKEN"] = "your-secret-token"  # Or set in .env file
```

If no `APP_TOKEN` is set, **authentication will be disabled** and the server will accept all requests.

---

## MCP Client Usage

<Tabs>
<Tab title="Local MCP Server">

This example shows how to use a local MCP server with stdio transport:

```python
import asyncio
from agents.mcp.server import MCPServerStdio, MCPServerStdioParams
from agency_swarm import Agency, Agent

# Set up local MCP server using stdio transport
stdio_server = MCPServerStdio(
    MCPServerStdioParams(
        command="python", # or "npx" if available
        # Path to your MCP server script or npx arguments
        args=["./path/to/your/mcp_server.py"]
    ),
    cache_tools_list=True
)

# Create agent with local MCP server
mcp_agent = Agent(
    name="LocalMCPAgent",
    mcp_servers=[stdio_server],
)

agency = Agency(mcp_agent)

async def run_local_example():
    await stdio_server.connect()
    response = await agency.get_response("List all mcp tools")
    print(response.final_output)
    await stdio_server.cleanup()

if __name__ == "__main__":
    asyncio.run(run_local_example())
```

</Tab>
<Tab title="Hosted MCP Server">

This example shows how to connect to a hosted MCP server over HTTP:

```python
import os
import asyncio
from agency_swarm import Agency, Agent, HostedMCPTool

# Create agent with hosted MCP server
mcp_agent = Agent(
    name="HostedMCPAgent",
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "mcp-tools-server",
                # Update with your hosted MCP server URL
                "server_url": "https://your-server.com/mcp/",
                "require_approval": "never",
                "headers": {
                    "Authorization": (
                        f"Bearer {os.getenv('APP_TOKEN', 'your-token')}"
                    )
                }
            }
        ),
    ],
)

agency = Agency(mcp_agent)

async def run_hosted_example():
    # HostedMCPTools do not require manual connection
    response = await agency.get_response("List all mcp tools")
    print(response.final_output)

if __name__ == "__main__":
    asyncio.run(run_hosted_example())
```

<Note>
For hosted MCP servers, ensure your server is accessible from the internet.
You can use tools like ngrok for local development.
</Note>

</Tab>
</Tabs>

## See Also

- [Agents SDK MCP guide](https://openai.github.io/openai-agents-python/mcp/)
- [Agents SDK MCP server reference](https://openai.github.io/openai-agents-python/ref/mcp/server/)