# How to integrate Mails so MCP with CrewAI

```json
{
  "title": "How to integrate Mails so MCP with CrewAI",
  "toolkit": "Mails so",
  "toolkit_slug": "mails_so",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/mails_so/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/mails_so/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:18:21.496Z"
}
```

## Introduction

This guide walks you through connecting Mails so to CrewAI using the Composio tool router. By the end, you'll have a working Mails so agent that can check if this email address is valid, validate a list of emails before campaign, download results for last batch validation job through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Mails so account through Composio's Mails so MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Mails so with

- [OpenAI Agents SDK](https://composio.dev/toolkits/mails_so/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/mails_so/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/mails_so/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/mails_so/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/mails_so/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/mails_so/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/mails_so/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/mails_so/framework/cli)
- [Google ADK](https://composio.dev/toolkits/mails_so/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/mails_so/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/mails_so/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/mails_so/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/mails_so/framework/llama-index)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Mails so connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Mails so
- Build a conversational loop where your agent can execute Mails so 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 Mails so MCP server, and what's possible with it?

The Mails so MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mails so account. It provides structured and secure access to advanced email validation services, so your agent can perform actions like verifying single emails, running bulk checks, and retrieving validation results on your behalf.
- Single email validation: Instantly check if an individual email address is valid and safe to use before sending important messages.
- Bulk email validation: Submit large lists of email addresses for automated deliverability and quality checks, keeping your contact databases clean.
- Batch results retrieval: Effortlessly fetch detailed validation outcomes for previously submitted bulk jobs, so you can review and act on the results.
- Email list hygiene automation: Use the agent to regularly scan and validate contact lists, reducing bounce rates and improving sender reputation.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `MAILS_SO_RETRIEVE_BATCH_RESULTS` | Retrieve Batch Results | Tool to retrieve results of a bulk validation job. Use after submitting a batch and you have its ID. |
| `MAILS_SO_VALIDATE_BULK_EMAILS` | Validate Bulk Emails | Tool to validate multiple email addresses in bulk. Use when you need to confirm deliverability of a list of emails at once. |
| `MAILS_SO_VALIDATE_SINGLE_EMAIL` | Validate Single Email | Tool to validate a single email address. Use when you need to check deliverability and quality details before sending an email. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Mails so MCP server is an implementation of the Model Context Protocol that connects your AI agent to Mails so. It provides structured and secure access so your agent can perform Mails so 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 Mails so 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 Mails so 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 Mails so 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 Mails so

**What's happening:**
- You create a Mails so only session through Composio
- Composio returns an MCP HTTP URL that exposes Mails so tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["mails_so"])

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=["mails_so"],
)
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 Mails so through Composio's Tool Router. The agent can perform Mails so 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 Mails so MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/mails_so/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/mails_so/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/mails_so/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/mails_so/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/mails_so/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/mails_so/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/mails_so/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/mails_so/framework/cli)
- [Google ADK](https://composio.dev/toolkits/mails_so/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/mails_so/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/mails_so/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/mails_so/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/mails_so/framework/llama-index)

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- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
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- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Mails so MCP?

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

### Can I manage the permissions and scopes for Mails so while using Tool Router?

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
