Documentation Index
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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.
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:
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:
import os
os.environ["APP_TOKEN"] = "your-secret-token" # Or set in .env file
APP_TOKEN is set, authentication will be disabled and the server will accept all requests.
MCP Client Usage
Local MCP Server
Hosted MCP Server
This example shows how to use a local MCP server with stdio transport: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())
This example shows how to connect to a hosted MCP server over HTTP: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())
For hosted MCP servers, ensure your server is accessible from the internet.
You can use tools like ngrok for local development.
See Also
