How to integrate Countdown api MCP with Autogen

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Introduction

This guide walks you through connecting Countdown api to AutoGen using the Composio tool router. By the end, you'll have a working Countdown api agent that can list all my ebay data collections, start processing requests for a collection, get autocomplete suggestions for 'wireless earbuds', delete a finished collection by its id through natural language commands.

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

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

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

The Countdown api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Countdown api account. It provides structured and secure access to real-time eBay marketplace data, so your agent can perform actions like searching eBay products, managing collections, retrieving seller feedback, and automating product data workflows on your behalf.

  • eBay product search and autocomplete: Instantly fetch eBay autocomplete suggestions and help agents surface relevant product search terms and ideas in real time.
  • Collection management and orchestration: Create, update, list, or delete collections to batch and organize multiple eBay data requests for streamlined marketplace analysis.
  • Automated collection processing: Start or clear queued requests within a collection, making it easy to control and automate data gathering operations from eBay.
  • Destination setup and notifications: Set up or remove destinations for results and notifications, ensuring your agent can manage where and how you receive processed eBay data.
  • Access to rich eBay metadata: Retrieve detailed collection information, product details, customer reviews, and seller feedback to power analytics and business decisions.

Supported Tools & Triggers

Tools
Clear Collection RequestsTool to clear all requests from a specified collection.
Create a new collectionTool to create a new collection.
Get CollectionTool to retrieve details for a single collection by ID.
List CollectionsTool to list all collections for the authenticated account.
Start CollectionTool to start processing a collection's queued requests.
Update an existing collectionTool to update an existing collection.
eBay AutocompleteTool to fetch eBay autocomplete suggestions.
Create DestinationTool to create a destination.
Delete CollectionTool to delete a collection and its configuration by ID.
Delete DestinationTool to delete a destination by ID.
Delete Single RequestTool to remove a specific request from a collection.
List DestinationsTool to list all destinations configured for the account.
Get Account InformationTool to retrieve account usage and current platform status.
Export Requests CSVTool to export all requests in a collection as CSV download links.
Export Requests as JSONTool to download all requests in a collection as JSON.
List Requests PagedTool to list requests for a collection by page.
Update Single RequestTool to modify parameters of an existing request in a collection.
Get Result SetTool to retrieve a collection run's result set payload.
List Result SetsTool to list result sets produced by a collection.
Resend Result Set WebhookTool to resend the webhook for a previously generated result set.
Stop All CollectionsTool to stop all collections.
Stop CollectionTool to stop (pause) a single collection’s processing by ID.
Update DestinationTool to update a destination's configuration by ID.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

Prerequisites

You will need:

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

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.

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 Countdown api via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Countdown api connections to use

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 Countdown api session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["countdown_api"]
    )
    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 Countdown api tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Countdown api assistant agent with MCP tools
    agent = AssistantAgent(
        name="countdown_api_assistant",
        description="An AI assistant that helps with Countdown api 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 Countdown api tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Countdown api 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 Countdown api 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 Countdown api 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 Countdown api session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["countdown_api"]
    )
    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 Countdown api assistant agent with MCP tools
        agent = AssistantAgent(
            name="countdown_api_assistant",
            description="An AI assistant that helps with Countdown api 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 Countdown api 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 Countdown api 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 Countdown api, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Countdown api MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Countdown api MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Countdown api MCP with Claude Agent SDK

Google ADK

Google ADK

Use Countdown api MCP with Google ADK

LangChain

LangChain

Use Countdown api MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Countdown api MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Countdown api MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Countdown api MCP with LlamaIndex

CrewAI

CrewAI

Use Countdown api MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Countdown api MCP?

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

Can I use Tool Router MCP with Autogen?

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 Countdown api tools.

Can I manage the permissions and scopes for Countdown api while using Tool Router?

Yes, absolutely. You can configure which Countdown api 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.

How safe is my data with Composio Tool Router?

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 Countdown api data and credentials are handled as safely as possible.

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