# How to integrate Melo MCP with Pydantic AI ```json { "title": "How to integrate Melo MCP with Pydantic AI", "toolkit": "Melo", "toolkit_slug": "melo", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/melo/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/melo/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:18:36.103Z" } ``` ## Introduction This guide walks you through connecting Melo to Pydantic AI using the Composio tool router. By the end, you'll have a working Melo agent that can find cities with active listings in paris region, suggest location matches for 'boulogne' input, list all saved property search queries through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Melo account through Composio's Melo MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Melo with - [OpenAI Agents SDK](https://composio.dev/toolkits/melo/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/melo/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/melo/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/melo/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/melo/framework/codex) - [OpenClaw](https://composio.dev/toolkits/melo/framework/openclaw) - [Hermes](https://composio.dev/toolkits/melo/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/melo/framework/cli) - [Google ADK](https://composio.dev/toolkits/melo/framework/google-adk) - [LangChain](https://composio.dev/toolkits/melo/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/melo/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/melo/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/melo/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/melo/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 Melo - 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 Melo 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 Melo MCP server, and what's possible with it? The Melo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Melo account. It provides structured and secure access to real-time, deduplicated French real estate listings and market analytics, so your agent can perform actions like searching cities, suggesting locations, retrieving saved searches, and testing webhook integrations on your behalf. - City discovery and filtering: Instantly fetch and filter lists of cities to explore specific property markets or narrow down search locations. - Location autocomplete and suggestions: Have your agent suggest matching locations for partial queries, ensuring accurate and user-friendly real estate searches. - Personalized search retrieval: Retrieve and manage your saved searches to quickly revisit or update your real estate criteria. - Webhook event simulation: Test and validate webhook integrations by simulating events, making real estate workflow automation seamless. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `MELO_GET_CITIES` | Get Cities | Tool to retrieve a list of cities with optional filters. Use when you need to fetch available cities before processing location-specific data. | | `MELO_LIST_SEARCHES` | List Searches | Retrieves all property searches saved by the authenticated user from the Melo API. Use this tool to: - View all saved property searches with their filtering criteria - Check notification settings for each search - Paginate through large lists of searches - Filter searches by title or notification status - Sort searches alphabetically by title Returns search details including location filters, budget ranges, property types, and notification configurations. The API uses Hydra collection format for pagination. | | `MELO_SIMULATE_WEBHOOK` | Simulate Webhook | Simulates sending a Melo webhook event to a specified endpoint for testing webhook integrations. This tool triggers the Melo API to send a sample webhook payload (either a property match or event notification) to your specified HTTPS endpoint. Useful for testing webhook handlers before going live. The API will send realistic property data including advert details, contact information, and geographic data to your endpoint. Use this when you need to test how your webhook endpoint handles Melo notifications. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Melo MCP server is an implementation of the Model Context Protocol that connects your AI agent to Melo. It provides structured and secure access so your agent can perform Melo 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 Melo - 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 Melo - MCPServerStreamableHTTP connects to the Melo 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 Melo 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 Melo composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["melo"], ) 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 Melo endpoint - The agent uses GPT-5 to interpret user commands and perform Melo operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent melo_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[melo_mcp], instructions=( "You are a Melo assistant. Use Melo 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 - Melo 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 Melo.\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 Melo composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["melo"], ) 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 melo_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[melo_mcp], instructions=( "You are a Melo assistant. Use Melo 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 Melo.\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 Melo through Composio's Tool Router. With this setup, your agent can perform real Melo 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 + Melo 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 Melo MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/melo/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/melo/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/melo/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/melo/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/melo/framework/codex) - [OpenClaw](https://composio.dev/toolkits/melo/framework/openclaw) - [Hermes](https://composio.dev/toolkits/melo/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/melo/framework/cli) - [Google ADK](https://composio.dev/toolkits/melo/framework/google-adk) - [LangChain](https://composio.dev/toolkits/melo/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/melo/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/melo/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/melo/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/melo/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Melo MCP? With a standalone Melo MCP server, the agents and LLMs can only access a fixed set of Melo tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Melo 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 Melo tools. ### Can I manage the permissions and scopes for Melo while using Tool Router? Yes, absolutely. You can configure which Melo 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 Melo data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)