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How to integrate Gagelist MCP with Autogen

Connect Autogen to Gagelist MCP. Add a new calibration record for this gage, generate calibration certificate for equipment id 2345, and more using natural language, with authentication handled for you.

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Gagelist is a calibration management platform with easy APIs and intuitive interfaces. It streamlines equipment tracking and compliance for quality-focused teams.

21 Tools

Introduction

This guide walks you through connecting Gagelist to AutoGen using the Composio tool router. By the end, you'll have a working Gagelist agent that can track gages and calibration records, generate calibration certificates, manage manufacturers, and upload attachments to gages through natural language commands.

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

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

Also integrate Gagelist with

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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 Gagelist
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Gagelist tools
  • Run a live chat loop where you ask the agent to perform Gagelist 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 Gagelist MCP server, and what's possible with it?

The Gagelist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Gagelist account. It provides structured and secure access to your calibration records and asset management workflows, so your agent can perform actions like adding new gages, managing calibration events, generating certificates, and retrieving account information on your behalf.

  • Seamless calibration record management: Direct your agent to add, update, or delete calibration records, keeping your asset compliance up-to-date with minimal manual effort.
  • Automated gage and manufacturer tracking: Have the agent add new gages or manufacturers to your Gagelist inventory, or remove outdated entries as your equipment changes.
  • Instant calibration certificate generation: Let your agent generate official PDF calibration certificates from existing records, streamlining audit and reporting processes.
  • Account insights and status checks: Quickly retrieve your account settings or overall status, giving you a real-time view into your calibration program's health.

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 Gagelist 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 Gagelist 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 Gagelist 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 Gagelist session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["gagelist"]
    )
    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 Gagelist 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 Gagelist assistant agent with MCP tools
    agent = AssistantAgent(
        name="gagelist_assistant",
        description="An AI assistant that helps with Gagelist 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 Gagelist 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 Gagelist 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 Gagelist 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 Gagelist 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 Gagelist session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["gagelist"]
    )
    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 Gagelist assistant agent with MCP tools
        agent = AssistantAgent(
            name="gagelist_assistant",
            description="An AI assistant that helps with Gagelist 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 Gagelist 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 Gagelist 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 Gagelist, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

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

Add Calibration Record

Creates a new calibration record in GageList to document equipment calibration results.

Add Gage Record

Tool to add a new gage record.

Add Manufacturer

Creates a manufacturer record in GageList for tracking companies that produce gages and measurement instruments.

Authenticate with Gagelist

Tool to obtain a Gagelist API access token.

Delete Calibration Record

Deletes a calibration record by its ID.

Delete Gage Record

Deletes a gage record by its ID.

Delete Manufacturer

Tool to delete a manufacturer by its ID.

Generate Calibration Certificate

Tool to generate a PDF certificate from a calibration record.

Get Account Settings

Tool to get account settings.

Get account status

Tool to retrieve account status.

Get all calibration records

Tool to retrieve a paginated list of calibration records.

Get All Gage Records

Tool to retrieve a paginated list of gage records.

Get All Manufacturers

Tool to retrieve a list of all manufacturers.

Get Attachment

Tool to retrieve an attachment by its ID.

Get Custom Fields

Tool to retrieve custom field definitions.

Get Single Calibration Record

Tool to retrieve details of a single calibration record.

Get Single Gage Record

Retrieves details of a single GageList gage record by its unique ID, including identification, calibration and measurement information, location, responsible user, test templates, and attachment metadata.

Update Account Settings

Updates GageList account settings using a complete settings payload.

Update Custom Field Values

Tool to update custom field values.

Update Manufacturer

Tool to update a manufacturer by its ID.

Upload Attachment To Gage Record

Tool to upload an attachment to a gage record.

FRAMEWORKS

How to build Gagelist MCP Agent with another framework

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Atomic Agent logo

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Google ADK logo

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Vercel AI SDK logo

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Mastra AI logo

Mastra AI

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LlamaIndex

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CrewAI logo

CrewAI

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Pydantic AI logo

Pydantic AI

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FAQ

Frequently asked questions

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