How to integrate Dovetail MCP with Pydantic AI

This guide walks you through connecting Dovetail to Pydantic AI 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 Pydantic AI 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.

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Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.

51 Tools

Introduction

This guide walks you through connecting Dovetail to Pydantic AI 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 Pydantic AI 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

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Dovetail
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Dovetail workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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.

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

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

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.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Dovetail
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Dovetail
  • MCPServerStreamableHTTP connects to the Dovetail MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Dovetail
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["dovetail"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
dovetail_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[dovetail_mcp],
    instructions=(
        "You are a Dovetail assistant. Use Dovetail tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Dovetail endpoint
  • The agent uses GPT-5 to interpret user commands and perform Dovetail operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Dovetail.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Dovetail API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Dovetail and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Dovetail
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["dovetail"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    dovetail_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[dovetail_mcp],
        instructions=(
            "You are a Dovetail assistant. Use Dovetail tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Dovetail.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Dovetail through Composio's Tool Router. With this setup, your agent can perform real Dovetail actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Dovetail for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Dovetail action and event your agent gets out of the box.

Create Channel

Creates a new channel in Dovetail to organize and collect feedback data.

Create Contact

Tool to create a new contact in Dovetail.

Create Data

Tool to create a data item in a Dovetail project with text content, title, and/or structured fields.

Create Data Point

Tool to create a data point within a channel.

Create Doc

Tool to create a doc in a Dovetail project with text content, title and/or custom fields.

Create Insight

Creates a new insight in Dovetail to store synthesized research findings, observations, or conclusions.

Create Note

Tool to create a note in a Dovetail project with text content, title and/or custom fields.

Create Project

Tool to create a new project in your Dovetail workspace.

Create Topic

Tool to create a new topic in a Dovetail channel.

Delete Channel

Tool to delete an existing channel.

Delete Data

Tool to delete an existing data item.

Delete Doc

Tool to delete an existing doc.

Delete Insight

Tool to delete an existing insight.

Delete Note

Tool to delete an existing note.

Delete Topic

Tool to delete an existing topic.

Export Data

Tool to export data in HTML or Markdown format.

Export Doc

Tool to export a doc in HTML or Markdown format.

Export Insight

Tool to export an insight in HTML or Markdown format.

Export Note

Tool to export a note from Dovetail in HTML or Markdown format.

Get Contact

Tool to retrieve details of a specific contact.

Get Data

Tool to retrieve details of a specific data item by ID.

Get Doc

Tool to retrieve details of a specific doc by ID.

Get File

Tool to retrieve details of a specific file by its ID.

Get Folder

Tool to retrieve details of a specific folder.

Get Insight

Tool to retrieve details of a specific insight by ID.

Get Note

Tool to retrieve details of a specific note.

Get Project

Tool to retrieve details of a specific project.

Get Token Info

Retrieves information about the current API token, including its unique identifier and the associated workspace subdomain.

Import Data File

Tool to import a public URL of a file as new data in Dovetail.

Import Doc File

Tool to import a public file URL as a new doc in Dovetail.

Import Insight from File

Tool to import a file from a public URL as a new insight in Dovetail.

Import Note File

Tool to import a file from a public URL as a new note in Dovetail.

List Contacts

Retrieves a paginated list of contacts from a Dovetail workspace.

List Data

Tool to list data items in Dovetail.

List Docs

Tool to list docs in a Dovetail workspace with optional filtering, sorting, and pagination.

List Folders

Tool to get a list of folders associated with a workspace.

List Highlights

List highlights from your Dovetail workspace with optional filtering and pagination.

List Insights

Tool to get a list of insights associated with a workspace.

List Notes

List notes in Dovetail workspace with optional pagination and sorting.

List Projects

Tool to list all projects in Dovetail.

List Tags

List all tags in the authenticated Dovetail workspace.

List User Docs

Tool to get a list of docs associated with a user in Dovetail.

List User Insights

List personal insights for a user in Dovetail.

Magic Search

Tool to perform a magic search across workspace data.

Update Channel

Tool to update an existing channel's title or context.

Update Contact

Tool to update an existing contact in Dovetail.

Update Data

Tool to update a data item in Dovetail.

Update Doc

Tool to update a doc in Dovetail.

Update Insight

Updates an existing insight in Dovetail, allowing you to modify the title and custom fields.

Update Note

Tool to update an existing note in Dovetail.

Update Topic

Tool to update an existing topic.

FRAMEWORKS

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

LangChain logo

LangChain

Use Dovetail MCP with LangChain

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

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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.

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Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

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CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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