# How to integrate Fullenrich MCP with CrewAI ```json { "title": "How to integrate Fullenrich MCP with CrewAI", "toolkit": "Fullenrich", "toolkit_slug": "fullenrich", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/fullenrich/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/fullenrich/framework/crew-ai.md", "updated_at": "2026-05-12T10:12:26.439Z" } ``` ## Introduction This guide walks you through connecting Fullenrich to CrewAI 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 CrewAI 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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Fullenrich connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Fullenrich - Build a conversational loop where your agent can execute Fullenrich operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## 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 agent to Fullenrich. It provides structured and secure access so your agent can perform Fullenrich operations on your behalf through a secure, permission-based interface. 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Fullenrich connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Fullenrich via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Fullenrich MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Fullenrich **What's happening:** - You create a Fullenrich only session through Composio - Composio returns an MCP HTTP URL that exposes Fullenrich tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["fullenrich"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["fullenrich"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Fullenrich through Composio's Tool Router. The agent can perform Fullenrich operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## 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) ## 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 CrewAI? Yes, you can. CrewAI 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)