How to integrate Baremetrics MCP with Autogen

This guide walks you through connecting Baremetrics to AutoGen using the Composio tool router. By the end, you'll have a working Baremetrics agent that can show monthly recurring revenue trends, find customers likely to churn, compare churn across subscription plans through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Baremetrics account through Composio's Baremetrics MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Baremetrics is a subscription analytics platform for recurring-revenue businesses. It helps teams track MRR, churn, customers, and revenue trends in one place.

8 Tools

Introduction

This guide walks you through connecting Baremetrics to AutoGen using the Composio tool router. By the end, you'll have a working Baremetrics agent that can show monthly recurring revenue trends, find customers likely to churn, compare churn across subscription plans through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Baremetrics account through Composio's Baremetrics MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Baremetrics with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Baremetrics
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Baremetrics tools
  • Run a live chat loop where you ask the agent to perform Baremetrics operations

What is AutoGen?

Autogen 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:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Baremetrics MCP server, and what's possible with it?

The Baremetrics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Baremetrics account. It provides structured and secure access to your subscription metrics, revenue data, customer insights, and account details, so your agent can analyze performance, compare trends, review cohorts, inspect recurring revenue records, and identify connected data sources on your behalf.

  • Business metric summaries: Have your agent review daily MRR, revenue, and customer counts across any inclusive date range.
  • Metric trends and comparisons: Let the agent track a selected metric over time and compare its performance with the prior period.
  • Customer and plan breakdowns: Direct your agent to explain how individual customers or plans contributed to a metric during a chosen period.
  • Cohort analysis: Instruct your agent to compare customer retention and revenue behavior across signup cohorts.
  • Recurring revenue records: Have your agent inspect customers, plans, subscriptions, charges, and refunds, review account details, or identify connected payment and Baremetrics data sources.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

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.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Baremetrics account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Baremetrics via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Baremetrics connections to use
5

Import dependencies and create Tool Router session

python
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 Baremetrics session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["baremetrics"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Baremetrics tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# 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 Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# 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 Baremetrics assistant agent with MCP tools
    agent = AssistantAgent(
        name="baremetrics_assistant",
        description="An AI assistant that helps with Baremetrics operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Baremetrics tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Baremetrics 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")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Baremetrics tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Baremetrics and AutoGen:

python
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 Baremetrics session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["baremetrics"]
    )
    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 Baremetrics assistant agent with MCP tools
        agent = AssistantAgent(
            name="baremetrics_assistant",
            description="An AI assistant that helps with Baremetrics 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 Baremetrics 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())

Conclusion

You now have an Autogen assistant wired into Baremetrics through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Baremetrics, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Baremetrics action and event your agent gets out of the box.

Get Account

Return the connected Baremetrics account identity, company, default currency, and calculation range.

Get Cohorts

Return Baremetrics cohort analysis for comparing customer retention and revenue behavior across signup cohorts.

Get Metric Breakdown

Return daily values for one Baremetrics metric broken down by contributing customers or plans over an inclusive date range.

Get Metric Summary

Return wide daily Baremetrics business metrics for an inclusive date range, including MRR, revenue, and customer counts in each daily row.

Get Metric Trend

Return the daily time series and prior-period comparison for one Baremetrics metric over an inclusive date range.

Get Recurring Revenue Resource

Get one customer, plan, subscription, charge, or refund by its OID within a Baremetrics source.

List Recurring Revenue Resources

List one page of customers, plans, subscriptions, charges, or refunds from a Baremetrics source, using only filters valid for the selected resource type.

List Sources

List one page of payment and Baremetrics API sources in the connected account and return source IDs required by source-scoped resource tools; use next_cursor to fetch later pages.

FRAMEWORKS

How to build Baremetrics MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Baremetrics MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Baremetrics MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Baremetrics MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Baremetrics MCP with Claude Cowork

Codex logo

Codex

Use Baremetrics MCP with Codex

OpenClaw logo

OpenClaw

Use Baremetrics MCP with OpenClaw

Hermes logo

Hermes

Use Baremetrics MCP with Hermes

CLI logo

CLI

Use Baremetrics MCP with CLI

Google ADK logo

Google ADK

Use Baremetrics MCP with Google ADK

LangChain logo

LangChain

Use Baremetrics MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Baremetrics MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Baremetrics MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Baremetrics MCP with LlamaIndex

CrewAI logo

CrewAI

Use Baremetrics MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Baremetrics MCP server, the agents and LLMs can only access a fixed set of Baremetrics tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Baremetrics and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Baremetrics tools.

Yes, absolutely. You can configure which Baremetrics scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Baremetrics data and credentials are handled as safely as possible.

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