# How to integrate Wachete MCP with Pydantic AI ```json { "title": "How to integrate Wachete MCP with Pydantic AI", "toolkit": "Wachete", "toolkit_slug": "wachete", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/wachete/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/wachete/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:29:55.603Z" } ``` ## Introduction This guide walks you through connecting Wachete to Pydantic AI using the Composio tool router. By the end, you'll have a working Wachete agent that can monitor a webpage for price changes, list all your active web watchers, delete a watcher monitoring an old url through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Wachete account through Composio's Wachete MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Wachete with - [OpenAI Agents SDK](https://composio.dev/toolkits/wachete/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/wachete/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/wachete/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/wachete/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/wachete/framework/codex) - [OpenClaw](https://composio.dev/toolkits/wachete/framework/openclaw) - [Hermes](https://composio.dev/toolkits/wachete/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/wachete/framework/cli) - [Google ADK](https://composio.dev/toolkits/wachete/framework/google-adk) - [LangChain](https://composio.dev/toolkits/wachete/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/wachete/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/wachete/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/wachete/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/wachete/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 Wachete - 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 Wachete 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 Wachete MCP server, and what's possible with it? The Wachete MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and more directly to your Wachete account. It provides structured and secure access to your web monitoring setup, so your agent can create watchers, monitor webpages for changes, manage your folders, and keep you notified about updates—all automatically. - Automated webpage monitoring: Let your agent create new watchers to track changes on any web page or specific elements, so you never miss an update. - Watcher management and cleanup: Effortlessly remove obsolete monitors by deleting watchers when you no longer need to track certain content. - Folder structure navigation: Retrieve and explore the content of your Wachete folders, listing all subfolders and active watchers for better organization. - Real-time change notifications: Instantly pull notifications about detected changes across all your monitored pages, keeping you up to date at a glance. - Comprehensive watcher overview: Ask your agent to list all configured watchers, making it easy to review, audit, or adjust your monitoring strategy as your needs evolve. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `WACHETE_CREATE_UPDATE_FOLDER` | Create or update folder | Create a new folder or update an existing folder in Wachete. Folders help organize watchers into hierarchical structures. Omit the id parameter to create a new folder, or provide an id to update an existing one. | | `WACHETE_CREATE_WATCHER` | Create Watcher | Create or update a Wachete watcher to monitor web page changes. Watchers check pages at specified intervals and send alerts when changes are detected. Use SinglePage mode for monitoring a single page, or Portal mode to crawl and monitor multiple linked pages. | | `WACHETE_DELETE_FOLDER` | Delete folder | Permanently deletes a folder along with all nested subfolders and watchers (monitoring tasks). This is a destructive operation that cannot be undone. Use when you need to remove an entire folder structure. All subfolders and monitoring tasks within the folder will be permanently deleted. Obtain the folder ID from the Get Folder Content action before calling. Example: "Delete the folder with ID 576b3f7e-e126-4e92-9b95-f72a8d187a18" | | `WACHETE_DELETE_WATCHER` | Delete watcher | Deletes a website monitoring watcher (task) by its unique ID. This operation is idempotent - deleting a non-existent or already-deleted watcher will succeed without error. Use when you need to permanently remove a monitoring task. Obtain the watcher ID from List Watchers or Create Watcher actions before calling. Example: "Delete the watcher with ID 974b65b5-6ccb-4996-812c-5a678c2455e8" | | `WACHETE_GET_CRAWLER_PAGES` | Get crawler pages | Retrieves all pages monitored by a crawler watcher (portal monitor). Use this to get detailed information about each page being tracked including URLs, last check timestamps, content changes, and error states. Only works with portal-type watchers that monitor multiple pages. | | `WACHETE_GET_DATA_HISTORY` | Get Data History | Retrieve history for a wachet (monitor). Returns timestamped snapshots of monitored content showing when changes occurred. Supports time range filtering and optional diff with previous value. Use continuationToken for pagination when retrieving large histories. | | `WACHETE_GET_FOLDER_CONTENT` | Get folder content | Retrieves the contents of a Wachete folder, including subfolders and watcher tasks. Use this tool to: - List all subfolders and tasks in the root folder (omit parentId) - List contents of a specific folder (provide parentId) - Navigate the folder hierarchy using the path breadcrumb - Check task statuses and last check data Returns subfolders, tasks with their monitoring details, folder path, and pagination token. | | `WACHETE_GET_WATCHER` | Get watcher by ID | Retrieve complete watcher (monitor) definition by ID. Use this to get detailed configuration and current status of a specific monitoring task including URL, XPath selector, alerts, notification endpoints, and latest check results. | | `WACHETE_LIST_NOTIFICATIONS` | List notifications | Retrieves notifications from Wachete watchers. Returns notifications for all watchers or filtered by specific watcher ID and/or time range. Useful for checking recent changes detected by your web page monitors. | | `WACHETE_LIST_WATCHERS` | List watchers | List all monitoring watchers (tasks) configured in your Wachete account. Optionally filter by search query. Returns up to 500 watchers with details including name, URL, monitoring settings, and notification configuration. | | `WACHETE_MOVE_ITEMS_TO_FOLDER` | Move Items to Folder | Move tasks (watchers) and folders to a specified destination folder. Use this to organize your monitoring structure by relocating items within the folder hierarchy. Provide at least one of folderIds or taskIds to move items. Set folderId to null to move items to root level. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Wachete MCP server is an implementation of the Model Context Protocol that connects your AI agent to Wachete. It provides structured and secure access so your agent can perform Wachete 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 Wachete - 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 Wachete - MCPServerStreamableHTTP connects to the Wachete 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 Wachete 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 Wachete composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["wachete"], ) 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 Wachete endpoint - The agent uses GPT-5 to interpret user commands and perform Wachete operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent wachete_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[wachete_mcp], instructions=( "You are a Wachete assistant. Use Wachete 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 - Wachete 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 Wachete.\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 Wachete composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["wachete"], ) 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 wachete_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[wachete_mcp], instructions=( "You are a Wachete assistant. Use Wachete 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 Wachete.\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 Wachete through Composio's Tool Router. With this setup, your agent can perform real Wachete 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 + Wachete 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 Wachete MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/wachete/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/wachete/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/wachete/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/wachete/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/wachete/framework/codex) - [OpenClaw](https://composio.dev/toolkits/wachete/framework/openclaw) - [Hermes](https://composio.dev/toolkits/wachete/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/wachete/framework/cli) - [Google ADK](https://composio.dev/toolkits/wachete/framework/google-adk) - [LangChain](https://composio.dev/toolkits/wachete/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/wachete/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/wachete/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/wachete/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/wachete/framework/crew-ai) ## Related Toolkits - [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations. - [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code. - [Bouncer](https://composio.dev/toolkits/bouncer) - Bouncer is an email validation platform that verifies the authenticity of email addresses in real-time and batch. It helps boost deliverability and reduce bounce rates for your communications. - [Conveyor](https://composio.dev/toolkits/conveyor) - Conveyor is a platform that automates security reviews with a Trust Center and AI-driven questionnaire automation. It streamlines compliance and vendor security processes for faster, hassle-free reviews. - [Crowdin](https://composio.dev/toolkits/crowdin) - Crowdin is a localization management platform that streamlines translation workflows and collaboration. It helps teams centralize multilingual content, boost productivity, and automate translation processes. - [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. - [Detrack](https://composio.dev/toolkits/detrack) - Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way. - [Dnsfilter](https://composio.dev/toolkits/dnsfilter) - Dnsfilter is a cloud-based DNS security and content filtering solution. It helps organizations block online threats and manage safe internet access with ease. - [Faraday](https://composio.dev/toolkits/faraday) - Faraday lets you embed AI in workflows across your stack for smarter automation. It boosts your favorite tools with actionable intelligence and seamless integration. - [Feathery](https://composio.dev/toolkits/feathery) - Feathery is an AI-powered platform for building dynamic data intake forms with advanced logic. It helps teams automate complex workflows and collect structured data with ease. - [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease. - [Formdesk](https://composio.dev/toolkits/formdesk) - Formdesk is an online form builder for creating and managing professional forms. It's perfect for collecting data, automating workflows, and integrating form submissions with your favorite services. - [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows. - [Graphhopper](https://composio.dev/toolkits/graphhopper) - GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses. - [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale. - [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals. - [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps. - [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination. - [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code. - [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Wachete MCP? With a standalone Wachete MCP server, the agents and LLMs can only access a fixed set of Wachete tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Wachete 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 Wachete tools. ### Can I manage the permissions and scopes for Wachete while using Tool Router? Yes, absolutely. You can configure which Wachete 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 Wachete data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)