# How to integrate Dovetail MCP with Pydantic AI ```json { "title": "How to integrate Dovetail MCP with Pydantic AI", "toolkit": "Dovetail", "toolkit_slug": "dovetail", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/dovetail/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/dovetail/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:09:39.806Z" } ``` ## 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 SDK](https://composio.dev/toolkits/dovetail/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/dovetail/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/dovetail/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/dovetail/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/dovetail/framework/codex) - [OpenClaw](https://composio.dev/toolkits/dovetail/framework/openclaw) - [Hermes](https://composio.dev/toolkits/dovetail/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/dovetail/framework/cli) - [Google ADK](https://composio.dev/toolkits/dovetail/framework/google-adk) - [LangChain](https://composio.dev/toolkits/dovetail/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/dovetail/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/dovetail/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/dovetail/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/dovetail/framework/crew-ai) ## 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. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DOVETAIL_CREATE_CHANNEL` | Create Channel | Creates a new channel in Dovetail to organize and collect feedback data. Channels are containers for specific types of customer feedback such as app reviews, NPS responses, churn reasons, product reviews, or support tickets. Use this to set up a new data collection source before importing feedback data. | | `DOVETAIL_CREATE_CONTACT` | Create Contact | Tool to create a new contact in Dovetail. Use when you need to register a contact before logging interactions. | | `DOVETAIL_CREATE_DATA` | Create Data | Tool to create a data item in a Dovetail project with text content, title, and/or structured fields. Use when you need to capture and store research data, interview notes, or other content in a project. | | `DOVETAIL_CREATE_DATA_POINT` | Create Data Point | Tool to create a data point within a channel. Use after capturing new content to record and classify it in Dovetail. | | `DOVETAIL_CREATE_DOC` | Create Doc | Tool to create a doc in a Dovetail project with text content, title and/or custom fields. Use when you need to document research findings, store notes, or create structured content within a project. The doc content is stored but not returned in the response. | | `DOVETAIL_CREATE_INSIGHT` | Create Insight | Creates a new insight in Dovetail to store synthesized research findings, observations, or conclusions. Use this tool when you need to document and save key findings from user research, interviews, or data analysis. Insights can optionally be linked to a project for better organization. Returns the created insight's ID, title, creation timestamp, and other metadata. Note: The body content is stored but not included in the response. | | `DOVETAIL_CREATE_NOTE` | Create Note | Tool to create a note in a Dovetail project with text content, title and/or custom fields. Use when you need to document research notes, store interview findings, or create structured content within a project. The note content is stored but not returned in the response. | | `DOVETAIL_CREATE_PROJECT` | Create Project | Tool to create a new project in your Dovetail workspace. Use when you need to create a project to organize research data. | | `DOVETAIL_CREATE_TOPIC` | Create Topic | Tool to create a new topic in a Dovetail channel. Requires channel_id, title, and description. Use to organize feedback within channels by creating themed discussion topics. | | `DOVETAIL_DELETE_CHANNEL` | Delete Channel | Tool to delete an existing channel. Use when you need to remove a channel and move it to the project's trash (restorable for 30 days). Confirm the channel ID before calling. | | `DOVETAIL_DELETE_DATA` | Delete Data | Tool to delete an existing data item. Use when you have confirmed the data ID and want to move it to trash (restorable for 30 days). Example: "Delete data with ID 1tFfvvAmYPCLUqb9zO8dgN." | | `DOVETAIL_DELETE_DOC` | Delete Doc | Tool to delete an existing doc. Use when you need to remove a doc and move it to the project's trash (restorable for 30 days). | | `DOVETAIL_DELETE_INSIGHT` | Delete Insight | Tool to delete an existing insight. Use when you have confirmed the insight ID and want to move it to trash (restorable for 30 days). | | `DOVETAIL_DELETE_NOTE` | Delete Note | Tool to delete an existing note. Use when you have confirmed the note ID and want to move it to trash (restorable for 30 days). | | `DOVETAIL_DELETE_TOPIC` | Delete Topic | Tool to delete an existing topic. Use when you have confirmed the topic ID and want to move it to trash (restorable for 30 days). Example: "Delete topic with ID 123e4567-e89b-12d3-a456-426614174000." | | `DOVETAIL_EXPORT_DATA` | Export Data | Tool to export data in HTML or Markdown format. Use when you need to retrieve a formatted version of data items from Dovetail. | | `DOVETAIL_EXPORT_DOC` | Export Doc | Tool to export a doc in HTML or Markdown format. Use when you need to retrieve the full content of a doc from Dovetail in a specific format. | | `DOVETAIL_EXPORT_INSIGHT` | Export Insight | Tool to export an insight in HTML or Markdown format. Use when you need to retrieve the full content of an insight for documentation, reporting, or sharing purposes. The exported content includes the insight's title and body in the specified format. | | `DOVETAIL_EXPORT_NOTE` | Export Note | Tool to export a note from Dovetail in HTML or Markdown format. Use when you need to retrieve the full content of a note in a specific export format. | | `DOVETAIL_GET_CONTACT` | Get Contact | Tool to retrieve details of a specific contact. Use when you have confirmed the contact ID and need full contact metadata from Dovetail. | | `DOVETAIL_GET_DATA` | Get Data | Tool to retrieve details of a specific data item by ID. Use when you have confirmed the data ID and need full metadata including custom fields, files, and project information from Dovetail. | | `DOVETAIL_GET_DOC` | Get Doc | Tool to retrieve details of a specific doc by ID. Use when you have confirmed the doc ID and need full doc metadata from Dovetail. | | `DOVETAIL_GET_FILE` | Get File | Tool to retrieve details of a specific file by its ID. Use when you need file metadata, download URL, or processing status from Dovetail. | | `DOVETAIL_GET_FOLDER` | Get Folder | Tool to retrieve details of a specific folder. Use when you have confirmed the folder ID and need full folder metadata from Dovetail. | | `DOVETAIL_GET_INSIGHT` | Get Insight | Tool to retrieve details of a specific insight by ID. Use when you need full insight metadata from Dovetail. | | `DOVETAIL_GET_NOTE` | Get Note | Tool to retrieve details of a specific note. Use when you have confirmed the note ID and need full note metadata from Dovetail. | | `DOVETAIL_GET_PROJECT` | Get Project | Tool to retrieve details of a specific project. Use when you have confirmed the project ID and need full project metadata from Dovetail. | | `DOVETAIL_GET_TOKEN_INFO` | Get Token Info | Retrieves information about the current API token, including its unique identifier and the associated workspace subdomain. Use this to verify which workspace the token belongs to. | | `DOVETAIL_IMPORT_DATA_FILE` | Import Data File | Tool to import a public URL of a file as new data in Dovetail. Use when you need to add external files to a project. | | `DOVETAIL_IMPORT_DOC_FILE` | Import Doc File | Tool to import a public file URL as a new doc in Dovetail. Use when you need to create a doc from an external file source. The file must be publicly accessible at the provided URL. | | `DOVETAIL_IMPORT_INSIGHT_FILE` | Import Insight from File | Tool to import a file from a public URL as a new insight in Dovetail. Use when you need to create an insight from an external file source such as PDFs, images, or documents. The file must be publicly accessible for Dovetail to fetch and import it. After import, the insight can be analyzed, tagged, and connected to projects. | | `DOVETAIL_IMPORT_NOTE_FILE` | Import Note File | Tool to import a file from a public URL as a new note in Dovetail. Use when you need to create a note by importing content from an accessible file URL (PDF, video, audio, etc.). | | `DOVETAIL_LIST_CONTACTS` | List Contacts | Retrieves a paginated list of contacts from a Dovetail workspace. Returns contact IDs, names, creation timestamps, and custom fields. Use cursor-based pagination (limit + start_cursor) to navigate large contact lists efficiently. | | `DOVETAIL_LIST_DATA` | List Data | Tool to list data items in Dovetail. Use when you need to retrieve, filter, sort, or paginate through your workspace data. Supports filtering by created_at (date range), project_id, and title. Results can be sorted by created_at or title. Uses cursor-based pagination with configurable page size. | | `DOVETAIL_LIST_DOCS` | List Docs | Tool to list docs in a Dovetail workspace with optional filtering, sorting, and pagination. Use when you need to retrieve docs, optionally filtered by project, title, content, or creation date. | | `DOVETAIL_LIST_FOLDERS` | List Folders | Tool to get a list of folders associated with a workspace. Use when you need to retrieve folder hierarchy, search for folders by title, or navigate the folder structure with pagination support. | | `DOVETAIL_LIST_HIGHLIGHTS` | List Highlights | List highlights from your Dovetail workspace with optional filtering and pagination. Use this action to retrieve highlights that have been created across your notes and projects. Supports filtering by project or note, and cursor-based pagination for large result sets. | | `DOVETAIL_LIST_INSIGHTS` | List Insights | Tool to get a list of insights associated with a workspace. Use when you need to retrieve insights with optional filtering by project, publication status, or title, and support for cursor-based pagination. | | `DOVETAIL_LIST_NOTES` | List Notes | List notes in Dovetail workspace with optional pagination and sorting. Use this tool to retrieve notes from your Dovetail workspace. Supports pagination for large result sets and sorting options. Returns note metadata including IDs, titles, timestamps, and associated project information. | | `DOVETAIL_LIST_PROJECTS` | List Projects | Tool to list all projects in Dovetail. Use after authenticating with a valid workspace token when you need to retrieve the full project list. | | `DOVETAIL_LIST_TAGS` | List Tags | List all tags in the authenticated Dovetail workspace. Returns tag details including title, color, highlight count, and timestamps. Supports pagination for workspaces with many tags. | | `DOVETAIL_LIST_USER_DOCS` | List User Docs | Tool to get a list of docs associated with a user in Dovetail. Use when you need to retrieve documents for a specific user or the authenticated user (use 'me' as user_id). | | `DOVETAIL_LIST_USER_INSIGHTS` | List User Insights | List personal insights for a user in Dovetail. Returns a paginated list of insights including their IDs, titles, creation dates, and published status. Use DOVETAIL_GET_TOKEN_INFO to obtain a valid user_id. | | `DOVETAIL_MAGIC_SEARCH` | Magic Search | Tool to perform a magic search across workspace data. Use when you need to retrieve relevant highlights, notes, insights, channels, themes, or tags by query. | | `DOVETAIL_UPDATE_CHANNEL` | Update Channel | Tool to update an existing channel's title or context. Use after confirming the channel ID and fields to change. | | `DOVETAIL_UPDATE_CONTACT` | Update Contact | Tool to update an existing contact in Dovetail. Use when you need to modify a contact's name, email, or custom fields. | | `DOVETAIL_UPDATE_DATA` | Update Data | Tool to update a data item in Dovetail. Use when you need to modify the title or fields of an existing data item. | | `DOVETAIL_UPDATE_DOC` | Update Doc | Tool to update a doc in Dovetail. Use when you need to modify a doc's title or custom fields. | | `DOVETAIL_UPDATE_INSIGHT` | Update Insight | Updates an existing insight in Dovetail, allowing you to modify the title and custom fields. Use when you need to revise insight information, correct titles, or update custom field values. | | `DOVETAIL_UPDATE_NOTE` | Update Note | Tool to update an existing note in Dovetail. Use when you need to modify a note's title, content, or custom fields. Example: "Update note 8IFq5LEC6hV1Vgsu0jPNJ with new title 'Q1 Review'". | | `DOVETAIL_UPDATE_TOPIC` | Update Topic | Tool to update an existing topic. Use after confirming the topic ID and fields to change. Example: "Update topic with id 123... to have title 'New'". | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Dovetail MCP server is an implementation of the Model Context Protocol that connects your AI agent to Dovetail. It provides structured and secure access so your agent can perform Dovetail 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 with an active API key - Basic familiarity with Python and async programming ### 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 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 ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables 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 ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies 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 ```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() ``` ### 5. Create a Tool Router Session 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 ```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") ``` ### 6. Initialize the Pydantic AI Agent 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 ```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." ), ) ``` ### 7. Build the chat interface 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 ```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() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```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. ## How to build Dovetail MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/dovetail/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/dovetail/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/dovetail/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/dovetail/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/dovetail/framework/codex) - [OpenClaw](https://composio.dev/toolkits/dovetail/framework/openclaw) - [Hermes](https://composio.dev/toolkits/dovetail/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/dovetail/framework/cli) - [Google ADK](https://composio.dev/toolkits/dovetail/framework/google-adk) - [LangChain](https://composio.dev/toolkits/dovetail/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/dovetail/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/dovetail/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/dovetail/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/dovetail/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [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. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. - [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly. ## Frequently Asked Questions ### 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 Pydantic AI? 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. ### 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. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)