# How to integrate Respond io MCP with Pydantic AI ```json { "title": "How to integrate Respond io MCP with Pydantic AI", "toolkit": "Respond io", "toolkit_slug": "respond_io", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/respond_io/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/respond_io/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:24:00.473Z" } ``` ## Introduction This guide walks you through connecting Respond io to Pydantic AI using the Composio tool router. By the end, you'll have a working Respond io agent that can add internal note to latest conversation, create a new contact named alex kim, list all channels connected to workspace through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Respond io account through Composio's Respond io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Respond io with - [OpenAI Agents SDK](https://composio.dev/toolkits/respond_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/respond_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/respond_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/respond_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/respond_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/respond_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/respond_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/respond_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/respond_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/respond_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/respond_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/respond_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/respond_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/respond_io/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 Respond io - 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 Respond io 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 Respond io MCP server, and what's possible with it? The Respond io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Respond io account. It provides structured and secure access to your customer conversation management platform, so your agent can perform actions like managing contacts, adding internal comments, creating and updating tags, and retrieving messages on your behalf. - Create and manage contacts: Easily have your agent add new customer contacts to your workspace, ensuring your CRM is always up to date. - Add internal comments to conversations: Let your agent insert internal notes into customer conversations, keeping your team informed and collaborating seamlessly. - Retrieve and organize channels: Direct your agent to list all messaging channels connected to your workspace, making it simple to audit or assign channels for support. - Tag and categorize conversations: Enable your agent to create new tags or update existing ones, helping you organize contacts and conversations for efficient follow-up. - Fetch specific messages: Ask your agent to pull up particular messages for review or context, streamlining support and follow-up actions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RESPOND_IO_CREATE_COMMENT` | Add internal comment to conversation | Tool to add a comment (internal note) to a contact's conversation. Use after verifying the contact identifier. | | `RESPOND_IO_CREATE_CONTACT` | Create Contact | Creates a new contact in the respond.io workspace with the specified details. The contact is identified by email, phone number, or contact ID. Supports adding profile information, language preferences, and custom fields that have been pre-configured in the workspace. | | `RESPOND_IO_CREATE_SPACE_TAG` | Create Space Tag | Creates a new tag in the Respond.io workspace for organizing and categorizing contacts and conversations. Tags help with segmentation, filtering, and workflow automation. Each tag must have a unique name within the workspace. | | `RESPOND_IO_GET_MESSAGE` | Get Message | Tool to retrieve a specific message. Use when you need the details of a message sent to or received from a contact. | | `RESPOND_IO_LIST_CHANNELS` | List channels | Tool to retrieve a list of channels connected to the workspace. Use when you need to enumerate all messaging channels with pagination support. | | `RESPOND_IO_LIST_USERS` | List users | Tool to retrieve a list of users in the workspace. Use when you need to fetch all workspace users for auditing or assignment. | | `RESPOND_IO_UPDATE_SPACE_TAG` | Update Space Tag | Updates an existing workspace tag by its current name. You can modify the tag's name, description, or emoji. Note: Color codes are not currently supported by the API and will be rejected if provided. At least one field besides currentName must be provided to update. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Respond io MCP server is an implementation of the Model Context Protocol that connects your AI agent to Respond io. It provides structured and secure access so your agent can perform Respond io 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 Respond io - 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 Respond io - MCPServerStreamableHTTP connects to the Respond io 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 Respond io 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 Respond io composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["respond_io"], ) 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 Respond io endpoint - The agent uses GPT-5 to interpret user commands and perform Respond io operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent respond_io_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[respond_io_mcp], instructions=( "You are a Respond io assistant. Use Respond io 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 - Respond io 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 Respond io.\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 Respond io composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["respond_io"], ) 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 respond_io_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[respond_io_mcp], instructions=( "You are a Respond io assistant. Use Respond io 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 Respond io.\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 Respond io through Composio's Tool Router. With this setup, your agent can perform real Respond io 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 + Respond io 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 Respond io MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/respond_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/respond_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/respond_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/respond_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/respond_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/respond_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/respond_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/respond_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/respond_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/respond_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/respond_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/respond_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/respond_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/respond_io/framework/crew-ai) ## Related Toolkits - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools. - [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations. - [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates. - [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates. - [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations. - [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code. - [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding. - [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls. - [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources. - [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates. - [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly. - [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time. - [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows. - [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. - [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster. - [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment. - [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support. - [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach. - [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback. - [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Respond io MCP? With a standalone Respond io MCP server, the agents and LLMs can only access a fixed set of Respond io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Respond io 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 Respond io tools. ### Can I manage the permissions and scopes for Respond io while using Tool Router? Yes, absolutely. You can configure which Respond io 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 Respond io data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)