# How to integrate Re amaze MCP with Pydantic AI ```json { "title": "How to integrate Re amaze MCP with Pydantic AI", "toolkit": "Re amaze", "toolkit_slug": "re_amaze", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/re_amaze/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/re_amaze/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:23:25.810Z" } ``` ## Introduction This guide walks you through connecting Re amaze to Pydantic AI using the Composio tool router. By the end, you'll have a working Re amaze agent that can list all tags used in recent reports, show most popular response templates for support, analyze tag trends from last week's conversations through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Re amaze account through Composio's Re amaze MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Re amaze with - [OpenAI Agents SDK](https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/re_amaze/framework/codex) - [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw) - [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/re_amaze/framework/cli) - [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk) - [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/re_amaze/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 Re amaze - 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 Re amaze 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 Re amaze MCP server, and what's possible with it? The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Re:amaze account. It provides structured and secure access to your customer support environment, so your agent can retrieve report tags, access canned response templates, and streamline support workflows for your team. - Tag usage analytics: Instantly pull all tags used in reports so your agent can analyze conversation trends, monitor support topics, or generate insights about ticket categorization. - Effortless response template retrieval: Fetch your brand's pre-built response templates, making it easy for the agent to suggest or automate consistent replies across channels. - Streamlined reply suggestions: Quickly surface relevant canned responses for agents or bots to use, speeding up customer replies and ensuring brand consistency. - Support workflow automation: Leverage tags and templates to help your agent automate repetitive customer support tasks and standardize communication processes. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RE_AMAZE_GET_REPORTS_TAGS` | Get report tags | Tool to retrieve a list of tags used in reports. Use when analyzing tag usage metrics across conversations. | | `RE_AMAZE_GET_RESPONSE_TEMPLATES` | Get Response Templates | Tool to retrieve response templates for the brand. Use when you need canned responses to streamline replies. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent to Re amaze. It provides structured and secure access so your agent can perform Re amaze 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 Re amaze - 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 Re amaze - MCPServerStreamableHTTP connects to the Re amaze 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 Re amaze 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 Re amaze composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["re_amaze"], ) 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 Re amaze endpoint - The agent uses GPT-5 to interpret user commands and perform Re amaze operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent re_amaze_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[re_amaze_mcp], instructions=( "You are a Re amaze assistant. Use Re amaze 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 - Re amaze 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 Re amaze.\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 Re amaze composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["re_amaze"], ) 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 re_amaze_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[re_amaze_mcp], instructions=( "You are a Re amaze assistant. Use Re amaze 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 Re amaze.\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 Re amaze through Composio's Tool Router. With this setup, your agent can perform real Re amaze 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 + Re amaze 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 Re amaze MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/re_amaze/framework/codex) - [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw) - [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/re_amaze/framework/cli) - [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk) - [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/re_amaze/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. 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Use it to quickly find and verify professional email addresses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Re amaze MCP? With a standalone Re amaze MCP server, the agents and LLMs can only access a fixed set of Re amaze tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Re amaze 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 Re amaze tools. ### Can I manage the permissions and scopes for Re amaze while using Tool Router? Yes, absolutely. You can configure which Re amaze 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 Re amaze data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)