# How to integrate Findymail MCP with CrewAI ```json { "title": "How to integrate Findymail MCP with CrewAI", "toolkit": "Findymail", "toolkit_slug": "findymail", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/findymail/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/findymail/framework/crew-ai.md", "updated_at": "2026-05-12T10:11:28.700Z" } ``` ## Introduction This guide walks you through connecting Findymail to CrewAI using the Composio tool router. By the end, you'll have a working Findymail agent that can find verified email for john at acme.com, create a new contact list for leads, verify deliverability of this email address through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Findymail account through Composio's Findymail MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Findymail with - [OpenAI Agents SDK](https://composio.dev/toolkits/findymail/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/findymail/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/findymail/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/findymail/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/findymail/framework/codex) - [OpenClaw](https://composio.dev/toolkits/findymail/framework/openclaw) - [Hermes](https://composio.dev/toolkits/findymail/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/findymail/framework/cli) - [Google ADK](https://composio.dev/toolkits/findymail/framework/google-adk) - [LangChain](https://composio.dev/toolkits/findymail/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/findymail/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/findymail/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/findymail/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Findymail connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Findymail - Build a conversational loop where your agent can execute Findymail 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 Findymail MCP server, and what's possible with it? The Findymail MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Findymail account. It provides structured and secure access to verified B2B contact data, so your agent can create and manage contact lists, find and verify emails, and streamline your outreach workflow automatically. - Automated contact list management: Let your agent create new contact lists, fetch all your lists, or delete lists as your prospecting needs change. - Precise contact discovery: Ask your agent to find and retrieve emails for prospects based on full name and company domain, making it easier to build targeted outreach campaigns. - Bulk contact retrieval: Direct your agent to list all contacts within a specific list, enabling quick access to leads for export or follow-up. - Email deliverability verification: Have your agent check if an email address is valid and safe to use before sending that crucial first message. - Seamless CRM enrichment: Use verified contact details to automatically enrich your CRM, helping you keep data accurate and actionable for your sales team. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FINDYMAIL_ADD_EXCLUDED_DOMAIN` | Add Excluded Domain | Tool to add domains to an exclusion list in Findymail. Use when you need to prevent email searches or verification for specific domains. | | `FINDYMAIL_CREATE_EXCLUSION_LIST` | Create Exclusion List | Tool to create a new exclusion list for Intellimatch searches. Use when you need to filter out specific companies or contacts from search results. | | `FINDYMAIL_CREATE_LIST` | Create Contact List | Tool to create a new contact list. Use when you need to organize contacts into a named list before adding them. | | `FINDYMAIL_DELETE_EXCLUSION_LIST` | Delete Exclusion List | Tool to permanently delete an exclusion list by its ID. Use when you need to remove an exclusion list from Intellimatch. This action is irreversible. | | `FINDYMAIL_DELETE_LIST` | Delete Contact List | Permanently deletes a contact list by its ID. This action is irreversible and will also remove all contacts in the list. Returns 404 if the list does not exist. | | `FINDYMAIL_FIND_EMAIL_BY_NAME` | Find Email by Name | Tool to find someone's email using their full name and company domain. Use when you have a person's name and domain and need their email address. Supports asynchronous search via webhook_url. | | `FINDYMAIL_GET_CONTACT_LISTS` | Get Contact Lists | Tool to retrieve all contact lists. Use when you need an overview of your existing Findymail lists. | | `FINDYMAIL_GET_CREDITS` | Get Credits | Tool to check available API credits for your Findymail account. Use when you need to verify remaining credits before performing operations. | | `FINDYMAIL_GET_CREDITS_SUMMARY` | Get Credits Summary | Tool to retrieve credits usage summary report for the authenticated account. Use when you need to check credits consumption over time. | | `FINDYMAIL_GET_CREDITS_TEAM_SUMMARY` | Get Credits Team Summary | Tool to retrieve team credits usage summary report. Use when you need an overview of credit consumption across team members. | | `FINDYMAIL_GET_EXCLUSION_LIST` | Get Exclusion List | Tool to retrieve a specific exclusion list by ID. Use when you need details about a particular exclusion list. | | `FINDYMAIL_GET_INTELLIMATCH_DATA` | Get Intellimatch Data | Tool to retrieve data from an Intellimatch search. Use after initiating a search with POST /api/intellimatch/search and confirming completion with GET /api/intellimatch/status. | | `FINDYMAIL_GET_INTELLIMATCH_STATUS` | Get Intellimatch Status | Tool to check the status of an Intellimatch search job. Use when you need to verify if an Intellimatch search has completed after initiating it. | | `FINDYMAIL_LIST_CONTACTS` | List Contacts | Tool to retrieve contacts from a specified list (paginated). Use after selecting a list to fetch its contacts. | | `FINDYMAIL_LIST_EXCLUDED_DOMAINS` | List Excluded Domains | Tool to retrieve domains excluded from Intellimatch searches. Use when you need to view the current domain exclusion list. | | `FINDYMAIL_LIST_EXCLUSION_LISTS` | List Exclusion Lists | Tool to retrieve all exclusion lists for managing excluded websites from Intellimatch searches. Use when you need to view configured website exclusions. | | `FINDYMAIL_REMOVE_EXCLUDED_DOMAIN` | Remove Excluded Domain | Tool to remove domains from the exclusion list. Use when you need to stop excluding specific domains from email search results. | | `FINDYMAIL_SEARCH_INTELLIMATCH` | Search Intellimatch | Tool to find companies and contacts using natural language queries. Use when you need to build targeted lead lists automatically by describing your ideal customer profile in plain language. | | `FINDYMAIL_UPDATE_EXCLUSION_LIST` | Update Exclusion List | Tool to update an existing exclusion list. Use when you need to rename or modify an exclusion list's properties. | | `FINDYMAIL_UPDATE_LIST` | Update Contact List | Tool to update an existing contact list. Use when you need to rename a list or change its sharing settings. | | `FINDYMAIL_VERIFY_EMAIL` | Verify Email | Tool to verify the deliverability of an email address. Use when you need to confirm an email can receive messages before outreach. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Findymail MCP server is an implementation of the Model Context Protocol that connects your AI agent to Findymail. It provides structured and secure access so your agent can perform Findymail 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 Findymail 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 Findymail 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 Findymail 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 Findymail **What's happening:** - You create a Findymail only session through Composio - Composio returns an MCP HTTP URL that exposes Findymail tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["findymail"]) 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=["findymail"], ) 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 Findymail through Composio's Tool Router. The agent can perform Findymail 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 Findymail MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/findymail/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/findymail/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/findymail/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/findymail/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/findymail/framework/codex) - [OpenClaw](https://composio.dev/toolkits/findymail/framework/openclaw) - [Hermes](https://composio.dev/toolkits/findymail/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/findymail/framework/cli) - [Google ADK](https://composio.dev/toolkits/findymail/framework/google-adk) - [LangChain](https://composio.dev/toolkits/findymail/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/findymail/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/findymail/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/findymail/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. - [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. - [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach. - [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 Findymail MCP? With a standalone Findymail MCP server, the agents and LLMs can only access a fixed set of Findymail tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Findymail 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 Findymail tools. ### Can I manage the permissions and scopes for Findymail while using Tool Router? Yes, absolutely. You can configure which Findymail 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 Findymail data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)