# How to integrate Fullenrich MCP with Autogen ```json { "title": "How to integrate Fullenrich MCP with Autogen", "toolkit": "Fullenrich", "toolkit_slug": "fullenrich", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/fullenrich/framework/autogen", "markdown_url": "https://composio.dev/toolkits/fullenrich/framework/autogen.md", "updated_at": "2026-05-12T10:12:26.439Z" } ``` ## Introduction This guide walks you through connecting Fullenrich to AutoGen using the Composio tool router. By the end, you'll have a working Fullenrich agent that can enrich this list of leads with emails and phones, check your fullenrich credit balance right now, get the latest status of bulk enrichment job through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Fullenrich account through Composio's Fullenrich MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Fullenrich with - [OpenAI Agents SDK](https://composio.dev/toolkits/fullenrich/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fullenrich/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fullenrich/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fullenrich/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fullenrich/framework/codex) - [OpenClaw](https://composio.dev/toolkits/fullenrich/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fullenrich/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fullenrich/framework/cli) - [Google ADK](https://composio.dev/toolkits/fullenrich/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fullenrich/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fullenrich/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fullenrich/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fullenrich/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fullenrich/framework/crew-ai) ## 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 Fullenrich - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Fullenrich tools - Run a live chat loop where you ask the agent to perform Fullenrich 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 Fullenrich MCP server, and what's possible with it? The Fullenrich MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fullenrich account. It provides structured and secure access to powerful B2B contact enrichment features, so your agent can perform actions like preparing contact lists, starting bulk enrichment jobs, retrieving batch results, and monitoring credit usage on your behalf. - Prepare and validate contact data lists: Guide your agent to create properly formatted lists of lead information for bulk enrichment, ensuring accuracy and readiness for processing. - Launch bulk enrichment jobs: Let your agent start large-scale enrichment tasks for up to 100 contacts at a time, aggregating verified emails and phone numbers from multiple vendors. - Retrieve bulk enrichment results: Automatically check on the status of ongoing jobs and fetch enriched contact data as soon as it's ready, streamlining your lead generation workflows. - Monitor workspace credit balance: Enable your agent to check your current API credit usage so you always know how many enrichment requests you have remaining. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FULLENRICH_CREATE_CONTACT_DATA_LIST` | Create Contact Data List | Tool to create a list of contact data entries. Use when preparing the 'datas' payload for bulk enrichment; validates each contact's composition and returns a JSON-ready list. | | `FULLENRICH_GET_CURRENT_CREDIT_BALANCE` | Get current credit balance | Tool to retrieve current workspace credit balance. Use after authenticating your API key. | | `FULLENRICH_FULLENRICH_GET_ENRICHMENT_RESULT` | Get Bulk Enrichment Result | Tool to retrieve results of a bulk enrichment by enrichment ID. Use after submitting a bulk enrichment job to check its status and get enriched data. | | `FULLENRICH_GET_REVERSE_EMAIL_RESULT` | Get Reverse Email Result | Tool to retrieve results from a reverse email lookup operation using reverse email ID. Use after submitting a reverse email lookup to check its status and get contact data. | | `FULLENRICH_REVERSE_EMAIL_LOOKUP` | Reverse Email Lookup | Tool to perform bulk reverse email lookup to retrieve full person and company profile from work or personal email addresses. Use when you have email addresses and need to enrich them with complete contact information. Results are processed asynchronously; use the returned enrichment_id to retrieve actual data. | | `FULLENRICH_SEARCH_COMPANY` | Search Company | Tool to search for companies based on filters including name, domain, industry, type, headquarters location, headcount, and founded year. Multiple filters within the same field are combined with OR logic. Use when you need to find companies matching specific criteria. | | `FULLENRICH_SEARCH_PEOPLE` | Search People | Tool to search for people based on filters including company, location, skills, position titles, and seniority levels. Multiple filters within the same field are combined with OR logic. Use when you need to find people matching specific professional criteria. | | `FULLENRICH_START_BULK_ENRICHMENT` | Start Bulk Enrichment | Tool to start a bulk enrichment job. Use when you have up to 100 contacts prepared and need batch enrichment. Use after confirming contact data. | | `FULLENRICH_VERIFY_API_KEY` | Verify API Key | Tool to check if your API key is valid and return the associated workspace ID. Use when you need to verify API key validity before performing other operations. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Fullenrich MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Fullenrich. Instead of manually wiring Fullenrich APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Fullenrich account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Fullenrich via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables 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 Fullenrich connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Fullenrich tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```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 Fullenrich session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["fullenrich"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen 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 ```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")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Fullenrich tools from the workbench ```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 Fullenrich assistant agent with MCP tools agent = AssistantAgent( name="fullenrich_assistant", description="An AI assistant that helps with Fullenrich operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Fullenrich 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 ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Fullenrich 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") ``` ## Complete Code ```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 Fullenrich session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["fullenrich"] ) 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 Fullenrich assistant agent with MCP tools agent = AssistantAgent( name="fullenrich_assistant", description="An AI assistant that helps with Fullenrich 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 Fullenrich 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 Fullenrich 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 Fullenrich, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Fullenrich MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/fullenrich/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fullenrich/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fullenrich/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fullenrich/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fullenrich/framework/codex) - [OpenClaw](https://composio.dev/toolkits/fullenrich/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fullenrich/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fullenrich/framework/cli) - [Google ADK](https://composio.dev/toolkits/fullenrich/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fullenrich/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fullenrich/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fullenrich/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fullenrich/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fullenrich/framework/crew-ai) ## Related Toolkits - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools. - [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations. - [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates. - [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates. - [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations. - [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code. - [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding. - [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls. - [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources. - [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates. - [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly. - [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time. - [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality. - [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster. - [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment. - [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support. - [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback. - [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Fullenrich MCP? With a standalone Fullenrich MCP server, the agents and LLMs can only access a fixed set of Fullenrich tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Fullenrich 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 Fullenrich tools. ### Can I manage the permissions and scopes for Fullenrich while using Tool Router? Yes, absolutely. You can configure which Fullenrich 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 Fullenrich data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)