# How to integrate Anonyflow MCP with Pydantic AI ```json { "title": "How to integrate Anonyflow MCP with Pydantic AI", "toolkit": "Anonyflow", "toolkit_slug": "anonyflow", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/anonyflow/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/anonyflow/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:01:14.414Z" } ``` ## Introduction This guide walks you through connecting Anonyflow to Pydantic AI using the Composio tool router. By the end, you'll have a working Anonyflow agent that can anonymize user email addresses before storage, deanonymize a list of encrypted ids, test if anonyflow api is reachable now through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Anonyflow account through Composio's Anonyflow MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Anonyflow with - [OpenAI Agents SDK](https://composio.dev/toolkits/anonyflow/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/anonyflow/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/anonyflow/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/anonyflow/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/anonyflow/framework/codex) - [OpenClaw](https://composio.dev/toolkits/anonyflow/framework/openclaw) - [Hermes](https://composio.dev/toolkits/anonyflow/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/anonyflow/framework/cli) - [Google ADK](https://composio.dev/toolkits/anonyflow/framework/google-adk) - [LangChain](https://composio.dev/toolkits/anonyflow/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/anonyflow/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/anonyflow/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/anonyflow/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/anonyflow/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 Anonyflow - 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 Anonyflow 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 Anonyflow MCP server, and what's possible with it? The Anonyflow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Anonyflow account. It provides structured and secure access to your data privacy tools, so your agent can anonymize values, recover original data, test API connectivity, and ensure compliance with privacy regulations on your behalf. - On-demand data anonymization: Instantly have your agent anonymize sensitive strings or lists of values before storage, sharing, or transmission to protect privacy. - Automated data deanonymization: Let your agent securely recover original values or data packets when needed, using your private key for authorized access only. - API connection health checks: Direct your agent to test and verify connectivity with the Anonyflow API before running critical privacy tasks. - Seamless privacy compliance workflows: Enable your agent to help maintain GDPR, CCPA, and HIPAA compliance by managing anonymization and deanonymization processes at scale. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ANONYFLOW_ANONYMIZE_PACKET` | Anonymize Packet | Tool to anonymize a JSON data packet with support for partial anonymization. Use when you need to conceal specific fields in structured data. If keys array is empty, the complete data packet will be anonymized. | | `ANONYFLOW_ANONYMIZE_VALUE` | Anonymize Value | Tool to anonymize a string or array of string values. Use when you need to conceal sensitive text before storage or transmission. Example: `AnonymizeValue().execute(AnonymizeValueRequest(data=['secret']))` Limitations: Only supports list of strings, not nested structures. | | `ANONYFLOW_DEANONYMIZE_PACKET` | Deanonymize Packet | Tool to deanonymize a JSON data packet using your private key. Use after receiving an anonymized packet to recover specific fields. | | `ANONYFLOW_DEANONYMIZE_VALUE` | Deanonymize Value | Tool to deanonymize one or more anonymized string values. Use when you need to recover the original plaintext values after encryption-based anonymization. Example: `DeanonymizeValue().execute(DeanonymizeValueRequest(data=[""]))` | | `ANONYFLOW_TEST_CONNECTION` | Test Connection | Tool to test the connection to the AnonyFlow API. Use when verifying that the AnonyFlow service is reachable and operational before performing anonymization tasks. Example: `TestConnection().execute(TestConnectionRequest())` | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Anonyflow MCP server is an implementation of the Model Context Protocol that connects your AI agent to Anonyflow. It provides structured and secure access so your agent can perform Anonyflow 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 Anonyflow - 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 Anonyflow - MCPServerStreamableHTTP connects to the Anonyflow 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 Anonyflow 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 Anonyflow composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["anonyflow"], ) 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 Anonyflow endpoint - The agent uses GPT-5 to interpret user commands and perform Anonyflow operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent anonyflow_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[anonyflow_mcp], instructions=( "You are a Anonyflow assistant. Use Anonyflow 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 - Anonyflow 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 Anonyflow.\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 Anonyflow composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["anonyflow"], ) 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 anonyflow_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[anonyflow_mcp], instructions=( "You are a Anonyflow assistant. Use Anonyflow 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 Anonyflow.\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 Anonyflow through Composio's Tool Router. With this setup, your agent can perform real Anonyflow 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 + Anonyflow 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 Anonyflow MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/anonyflow/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/anonyflow/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/anonyflow/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/anonyflow/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/anonyflow/framework/codex) - [OpenClaw](https://composio.dev/toolkits/anonyflow/framework/openclaw) - [Hermes](https://composio.dev/toolkits/anonyflow/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/anonyflow/framework/cli) - [Google ADK](https://composio.dev/toolkits/anonyflow/framework/google-adk) - [LangChain](https://composio.dev/toolkits/anonyflow/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/anonyflow/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/anonyflow/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/anonyflow/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/anonyflow/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 Anonyflow MCP? With a standalone Anonyflow MCP server, the agents and LLMs can only access a fixed set of Anonyflow tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Anonyflow 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 Anonyflow tools. ### Can I manage the permissions and scopes for Anonyflow while using Tool Router? Yes, absolutely. You can configure which Anonyflow 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 Anonyflow data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)