How to integrate Dovetail MCP with LangChain

Trusted by
AWS
Glean
Zoom
Airtable
GET STARTED FOR FREEGET A DEMO

30 min · no commitment · see it on your stack

Dovetail logo
LangChain logo
divider

Introduction

This guide walks you through connecting Dovetail to LangChain using the Composio tool router. By the end, you'll have a working Dovetail agent that can summarize all data points for project x, create a new insight from interview notes, list every contact added this month through natural language commands.

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

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

Also integrate Dovetail with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Dovetail project to Composio
  • Create a Tool Router MCP session for Dovetail
  • Initialize an MCP client and retrieve Dovetail tools
  • Build a LangChain agent that can interact with Dovetail
  • 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 Dovetail MCP server, and what's possible with it?

The Dovetail MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Dovetail account. It provides structured and secure access to your research workspace, so your agent can perform actions like creating insights, managing contacts, organizing channels, and retrieving research notes on your behalf.

  • Automated insight creation: Let your agent synthesize findings and store new insights in your Dovetail projects, streamlining your research analysis workflow.
  • Channel and topic management: Easily create, organize, or delete channels and topics to keep your research data structured and accessible.
  • Contact management and retrieval: Automatically add new research contacts or list all contacts in your workspace for better respondent tracking.
  • Research note access: Ask your agent to fetch detailed information about specific notes, giving you instant access to key research materials.
  • Data point recording and classification: Capture and categorize new data points within channels, ensuring every piece of feedback or observation is logged and ready for analysis.

Supported Tools & Triggers

Tools
Create ChannelCreates a new channel in Dovetail to organize and collect feedback data.
Create ContactTool to create a new contact in Dovetail.
Create DataTool to create a data item in a Dovetail project with text content, title, and/or structured fields.
Create Data PointTool to create a data point within a channel.
Create DocTool to create a doc in a Dovetail project with text content, title and/or custom fields.
Create InsightCreates a new insight in Dovetail to store synthesized research findings, observations, or conclusions.
Create NoteTool to create a note in a Dovetail project with text content, title and/or custom fields.
Create ProjectTool to create a new project in your Dovetail workspace.
Create TopicTool to create a new topic in a Dovetail channel.
Delete ChannelTool to delete an existing channel.
Delete DataTool to delete an existing data item.
Delete DocTool to delete an existing doc.
Delete InsightTool to delete an existing insight.
Delete NoteTool to delete an existing note.
Delete TopicTool to delete an existing topic.
Export DataTool to export data in HTML or Markdown format.
Export DocTool to export a doc in HTML or Markdown format.
Export InsightTool to export an insight in HTML or Markdown format.
Export NoteTool to export a note from Dovetail in HTML or Markdown format.
Get ContactTool to retrieve details of a specific contact.
Get DataTool to retrieve details of a specific data item by ID.
Get DocTool to retrieve details of a specific doc by ID.
Get FileTool to retrieve details of a specific file by its ID.
Get FolderTool to retrieve details of a specific folder.
Get InsightTool to retrieve details of a specific insight by ID.
Get NoteTool to retrieve details of a specific note.
Get ProjectTool to retrieve details of a specific project.
Get Token InfoRetrieves information about the current API token, including its unique identifier and the associated workspace subdomain.
Import Data FileTool to import a public URL of a file as new data in Dovetail.
Import Doc FileTool to import a public file URL as a new doc in Dovetail.
Import Insight from FileTool to import a file from a public URL as a new insight in Dovetail.
Import Note FileTool to import a file from a public URL as a new note in Dovetail.
List ContactsRetrieves a paginated list of contacts from a Dovetail workspace.
List DataTool to list data items in Dovetail.
List DocsTool to list docs in a Dovetail workspace with optional filtering, sorting, and pagination.
List FoldersTool to get a list of folders associated with a workspace.
List HighlightsList highlights from your Dovetail workspace with optional filtering and pagination.
List InsightsTool to get a list of insights associated with a workspace.
List NotesList notes in Dovetail workspace with optional pagination and sorting.
List ProjectsTool to list all projects in Dovetail.
List TagsList all tags in the authenticated Dovetail workspace.
List User DocsTool to get a list of docs associated with a user in Dovetail.
List User InsightsList personal insights for a user in Dovetail.
Magic SearchTool to perform a magic search across workspace data.
Update ChannelTool to update an existing channel's title or context.
Update ContactTool to update an existing contact in Dovetail.
Update DataTool to update a data item in Dovetail.
Update DocTool to update a doc in Dovetail.
Update InsightUpdates an existing insight in Dovetail, allowing you to modify the title and custom fields.
Update NoteTool to update an existing note in Dovetail.
Update TopicTool to update an existing topic.

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 Dovetail 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 Dovetail tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Dovetail 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 Dovetail tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "dovetail-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 Dovetail MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Dovetail 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 Dovetail 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 Dovetail 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=['dovetail']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "dovetail-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 Dovetail 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 Dovetail 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.

How to build Dovetail MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Dovetail MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Dovetail MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Dovetail MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Dovetail MCP with Claude Cowork

Codex logo

Codex

Use Dovetail MCP with Codex

OpenClaw logo

OpenClaw

Use Dovetail MCP with OpenClaw

Hermes logo

Hermes

Use Dovetail MCP with Hermes

CLI logo

CLI

Use Dovetail MCP with CLI

Google ADK logo

Google ADK

Use Dovetail MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Dovetail MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Dovetail MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Dovetail MCP with LlamaIndex

CrewAI logo

CrewAI

Use Dovetail MCP with CrewAI

FAQ

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

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

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

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

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Get started for freeGet a demo↗