How to integrate Missive MCP with LangChain

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Introduction

This guide walks you through connecting Missive to LangChain using the Composio tool router. By the end, you'll have a working Missive agent that can list all team members for marketing, create a draft email for client follow-up, send a chat message in project conversation through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Missive account through Composio's Missive MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Missive with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Missive project to Composio
  • Create a Tool Router MCP session for Missive
  • Initialize an MCP client and retrieve Missive tools
  • Build a LangChain agent that can interact with Missive
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Missive MCP server, and what's possible with it?

The Missive MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Missive account. It provides structured and secure access to your team's shared inboxes and chat threads, so your agent can perform actions like drafting emails, sending messages, generating reports, and managing team communication on your behalf.

  • Automated message drafting and scheduling: Let your agent create and save email, SMS, WhatsApp, or live chat drafts for later editing or scheduled sending.
  • Instant message sending in conversations: Have your agent send new messages directly to any Missive conversation, keeping your team in the loop in real time.
  • Team and user management: Effortlessly list all teams and their members, or pull a full directory of users in your Missive organization for easy coordination and task assignment.
  • Analytics report generation: Direct your agent to create detailed analytics reports across time ranges and filters, helping your team track productivity and engagement.
  • Webhook automation setup: Enable your agent to create or delete webhook subscriptions, so you can automate notifications and integrations with other tools as needed.

Supported Tools & Triggers

Tools
Create Analytics ReportTool to create an analytics report.
Create Missive ContactsTool to create one or more contacts in a Missive contact book.
Create DraftTool to create a new draft in Missive.
Create Missive PostTool to create a post in a Missive conversation.
Create Canned ResponseTool to create one or more canned responses (templates) in Missive.
Create Shared LabelTool to create one or more shared labels at the organization level.
Create Missive TaskTool to create a task in Missive.
Create TeamTool to create a new team in an organization.
Create WebhookTool to create a webhook subscription.
Delete DraftTool to delete a draft from a conversation by draft ID.
Delete PostTool to delete a post from a conversation by post ID.
Delete Saved ResponsesTool to delete one or more saved responses by ID.
Delete WebhookTool to delete a webhook subscription by webhook ID.
Get Analytics ReportTool to fetch a completed analytics report using its ID.
Get Missive ContactTool to fetch a specific contact using the contact ID.
Get Missive ConversationTool to fetch full conversation metadata (assignees/users/labels/team/org) for a specific conversation ID.
List Conversation MessagesTool to list messages belonging to a Missive conversation (newest first).
Get Missive MessageTool to fetch full message details including headers, HTML body, and attachments.
Get Missive ResponseTool to fetch a specific saved response using the response ID.
Get Missive TaskTool to get a single task by ID with full details including assignees, team, and conversation info.
List Missive Contact BooksTool to list contact books the authenticated user has access to.
List Missive Contact GroupsTool to list contact groups or organizations linked to a contact book.
List Missive ContactsTool to list contacts from a contact book.
List Conversation CommentsTool to list comments in a Missive conversation ordered from newest to oldest.
List Conversation DraftsTool to list draft messages in a Missive conversation (newest first).
List Conversation PostsTool to list posts in a Missive conversation ordered by newest first.
List Missive ConversationsTool to list conversations visible to the authenticated user ordered by newest activity first.
List Messages by Message-IDTool to fetch messages matching an email Message-ID header.
List Missive OrganizationsTool to list organizations the authenticated user is part of.
List Missive Saved ResponsesTool to list saved responses (canned responses/templates) for the authenticated user.
List Missive Shared LabelsTool to list shared labels (organization-level labels) available to the authenticated user.
List Missive TasksTool to list tasks accessible to the authenticated user.
List Missive TeamsTool to list all teams.
List Missive UsersTool to list all users.
Merge Missive ConversationsTool to merge multiple conversations into one.
Update Missive ContactTool to update one or more contacts in Missive.
Update Saved ResponseTool to update one or more saved responses in Missive.
Update Shared LabelsTool to update one or more shared labels in Missive.
Update Missive TaskTool to update an existing task's attributes in Missive.
Update Missive TeamTool to update one or more teams in Missive.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK 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

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Missive functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Missive tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Missive
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['missive']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Missive tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Missive tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "missive-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Missive MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Missive tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Missive related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Missive and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['missive']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "missive-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Missive related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Missive through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

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

With a standalone Missive MCP server, the agents and LLMs can only access a fixed set of Missive tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Missive and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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 Missive tools.

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

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

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