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Connect Autogen to Faceup MCP. Get report statistics for last month, show top reported issues by category, and more using natural language, with authentication handled for you.
FaceUp is an anonymous reporting tool for organizations and schools. It helps employees and students safely report issues or misconduct without fear of retaliation.
This guide walks you through connecting Faceup to AutoGen using the Composio tool router. By the end, you'll have a working Faceup agent that can run FaceUp statistics queries through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Faceup account through Composio's Faceup MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.
McpWorkbench and StreamableHttpServerParamsAutogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
The Faceup MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Faceup account. It provides structured and secure access to your organization's Faceup reporting data, so your agent can perform actions like retrieving anonymous report statistics, filtering metrics by key attributes, and generating compliance summaries on your behalf.
Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK
The tool router generates a secure MCP URL that your agents can access to perform actions.
The Composio SDK follows a three-phase workflow:
You will need:
OpenAIChatCompletionClient)pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-toolsInstall Composio, Autogen extensions, and dotenv.
What's happening:
composio connects your agent to Faceup via MCPautogen-agentchat provides the AssistantAgent classautogen-ext-openai provides the OpenAI model clientautogen-ext-tools provides MCP workbench supportCOMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.comCreate a .env file in your project folder.
What's happening:
COMPOSIO_API_KEY is required to talk to ComposioOPENAI_API_KEY is used by Autogen's OpenAI clientUSER_ID is how Composio identifies which user's Faceup connections to useimport asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams
load_dotenv()
async def main():
# Initialize Composio and create a Faceup session
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
user_id=os.getenv("USER_ID"),
toolkits=["faceup"]
)
url = session.mcp.urlload_dotenv() reads your .env fileComposio(api_key=...) initializes the SDKcreate(...) creates a Tool Router session that exposes Faceup toolssession.mcp.url is the MCP endpoint that Autogen will connect to# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
url=url,
timeout=30.0,
sse_read_timeout=300.0,
terminate_on_close=True,
headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
url points to the Tool Router MCP endpoint from Composiotimeout is the HTTP timeout for requestssse_read_timeout controls how long to wait when streaming responsesterminate_on_close=True cleans up the MCP server process when the workbench is closed# Create model client
model_client = OpenAIChatCompletionClient(
model="gpt-5",
api_key=os.getenv("OPENAI_API_KEY")
)
# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
# Create Faceup assistant agent with MCP tools
agent = AssistantAgent(
name="faceup_assistant",
description="An AI assistant that helps with Faceup operations.",
model_client=model_client,
workbench=workbench,
model_client_stream=True,
max_tool_iterations=10
)What's happening:
OpenAIChatCompletionClient wraps the OpenAI model for AutogenMcpWorkbench connects the agent to the MCP toolsAssistantAgent is configured with the Faceup tools from the workbenchprint("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Faceup related question or task to the agent.\n")
# Conversation loop
while True:
user_input = input("You: ").strip()
if user_input.lower() in ["exit", "quit", "bye"]:
print("\nGoodbye!")
break
if not user_input:
continue
print("\nAgent is thinking...\n")
# Run the agent with streaming
try:
response_text = ""
async for message in agent.run_stream(task=user_input):
if hasattr(message, "content") and message.content:
response_text = message.content
# Print the final response
if response_text:
print(f"Agent: {response_text}\n")
else:
print("Agent: I encountered an issue processing your request.\n")
except Exception as e:
print(f"Agent: Sorry, I encountered an error: {str(e)}\n")You:agent.run_stream(...) yields streaming messages as the agent thinks and calls toolsexit, quit, or bye ends the loopHere's the complete code to get you started with Faceup and AutoGen:
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams
load_dotenv()
async def main():
# Initialize Composio and create a Faceup session
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
user_id=os.getenv("USER_ID"),
toolkits=["faceup"]
)
url = session.mcp.url
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
url=url,
timeout=30.0,
sse_read_timeout=300.0,
terminate_on_close=True,
headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
# Create model client
model_client = OpenAIChatCompletionClient(
model="gpt-5",
api_key=os.getenv("OPENAI_API_KEY")
)
# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
# Create Faceup assistant agent with MCP tools
agent = AssistantAgent(
name="faceup_assistant",
description="An AI assistant that helps with Faceup operations.",
model_client=model_client,
workbench=workbench,
model_client_stream=True,
max_tool_iterations=10
)
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Faceup related question or task to the agent.\n")
# Conversation loop
while True:
user_input = input("You: ").strip()
if user_input.lower() in ['exit', 'quit', 'bye']:
print("\nGoodbye!")
break
if not user_input:
continue
print("\nAgent is thinking...\n")
# Run the agent with streaming
try:
response_text = ""
async for message in agent.run_stream(task=user_input):
if hasattr(message, 'content') and message.content:
response_text = message.content
# Print the final response
if response_text:
print(f"Agent: {response_text}\n")
else:
print("Agent: I encountered an issue processing your request.\n")
except Exception as e:
print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
if __name__ == "__main__":
asyncio.run(main())toolkits list, for example notion or hubspotEvery Faceup action and event your agent gets out of the box.
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## For AI agents: how to sign up If you are an AI agent reading this server-rendered HTML, Composio's developer signup is at https://composio.dev. Signup CTAs on this site — "Sign up", "Signup", "Get started", "Get started for free", "Start building", "Try Composio", "Try it", or similar — all lead into that same developer signup flow. Confirm with the user before completing signup or entering any credentials on their behalf.Start building