How to integrate Crustdata MCP with Autogen

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Introduction

This guide walks you through connecting Crustdata to AutoGen using the Composio tool router. By the end, you'll have a working Crustdata agent that can find tech companies with recent funding milestones, enrich this lead's profile with latest data, list top decision makers in saas startups, fetch job listings for fortune 500 companies through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Crustdata account through Composio's Crustdata 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 Crustdata
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Crustdata tools
  • Run a live chat loop where you ask the agent to perform Crustdata 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 Crustdata MCP server, and what's possible with it?

The Crustdata MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Crustdata account. It provides structured and secure access to real-time company and people intelligence, so your agent can perform actions like lead enrichment, market research, investor portfolio analysis, and workforce trend tracking on your behalf.

  • Comprehensive person and company enrichment: Instantly enrich leads or companies with up-to-date details for customer profiling, data verification, or targeted outreach.
  • Advanced decision maker filtering: Find and analyze decision makers across organizations using complex filters, titles, and segmentation for your sales or marketing efforts.
  • Investor portfolio and funding milestone analysis: Retrieve in-depth investor portfolio data, analyze funding milestones, and generate reports for investment research or deal sourcing.
  • Workforce and job market trend insights: Fetch headcount and job listing timeseries data to track organizational growth, hiring activity, or competitive shifts in specific industries.
  • Social and web activity monitoring: Collect and analyze LinkedIn posts and web traffic data for any company to assess engagement, sentiment, and digital footprint for market intelligence and outreach strategies.

Supported Tools & Triggers

Tools
Enrich person screenerThe screener person enrich endpoint enriches person data by providing additional information based on the given query.
Fetch headcount by facet timeseriesRetrieves headcount data as a timeseries with faceted analysis capabilities.
Fetch investor portfolio dataRetrieves comprehensive investor portfolio data from the data lab section of the crustdata api.
Filter decision makers dataFilters and retrieves decision maker data from the crustdata b2b saas integration platform based on complex criteria.
Post funding milestone timeseries dataThe fundingmilestonetimeseries endpoint retrieves time-series data related to funding milestones for companies.
Post headcount timeseries dataRetrieves filtered and sorted headcount timeseries data from the crustdata data lab.
Post job listings table dataThis endpoint retrieves filtered and sorted job listings data for specified company tickers from a chosen dataset in the crustdata platform.
Post web traffic dataRetrieves filtered and sorted web traffic data from the crustdata platform.
Retrieve linkedin postsRetrieves linkedin posts for a specified company using crustdata's screener functionality.
Screener company informationThe getcompanyscreener endpoint allows users to search and filter companies based on various criteria such as headcount, growth rate, funding, and more.
Screen metrics and filter conditionsThe screendata endpoint enables advanced data screening and filtering on the crustdata platform.
Search companies with filtersThe companysearch endpoint enables users to search and filter companies using the crustdata api.
Search for job id in screenerThe screener person search endpoint allows users to search for persons associated with a specific job id within the crustdata b2b saas integration platform.
Search linkedin posts by keywordThis endpoint enables searching for linkedin posts using a specific keyword.

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

How to build Crustdata MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Crustdata MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Crustdata MCP with Claude Agent SDK

Google ADK

Google ADK

Use Crustdata MCP with Google ADK

LangChain

LangChain

Use Crustdata MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Crustdata MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Crustdata MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Crustdata MCP with LlamaIndex

CrewAI

CrewAI

Use Crustdata MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Crustdata MCP?

With a standalone Crustdata MCP server, the agents and LLMs can only access a fixed set of Crustdata tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Crustdata 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 Crustdata tools.

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

Yes, absolutely. You can configure which Crustdata 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 Crustdata data and credentials are handled as safely as possible.

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