# How to integrate Platerecognizer MCP with Pydantic AI ```json { "title": "How to integrate Platerecognizer MCP with Pydantic AI", "toolkit": "Platerecognizer", "toolkit_slug": "platerecognizer", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/platerecognizer/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/platerecognizer/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:22:18.033Z" } ``` ## Introduction This guide walks you through connecting Platerecognizer to Pydantic AI using the Composio tool router. By the end, you'll have a working Platerecognizer agent that can show your alpr usage stats for june, how many plates did we scan this month?, get daily snapshot recognition call counts through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Platerecognizer account through Composio's Platerecognizer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Platerecognizer with - [OpenAI Agents SDK](https://composio.dev/toolkits/platerecognizer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/platerecognizer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/platerecognizer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/platerecognizer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/platerecognizer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/platerecognizer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/platerecognizer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/platerecognizer/framework/cli) - [Google ADK](https://composio.dev/toolkits/platerecognizer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/platerecognizer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/platerecognizer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/platerecognizer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/platerecognizer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/platerecognizer/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 Platerecognizer - 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 Platerecognizer 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 Platerecognizer MCP server, and what's possible with it? The Platerecognizer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Platerecognizer account. It provides structured and secure access to your license plate recognition data, so your agent can monitor usage, retrieve recognition statistics, track monthly activity, and help you stay on top of your ALPR operations. - Monitor monthly recognition usage: Instantly check how many snapshot recognition calls you've made during the current month to manage your account limits. - Retrieve up-to-date usage statistics: Ask your agent for real-time statistics on your Platerecognizer snapshot API activity to spot trends or anomalies. - Automate usage tracking: Set up workflows where your agent periodically fetches and summarizes ALPR statistics for compliance or reporting. - Stay informed on API consumption: Let your agent proactively notify you as you approach usage thresholds, helping you avoid interruptions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PLATERECOGNIZER_READ_LICENSE_PLATE` | Read License Plate | Tool to read license plates from images with confidence scores and optional vehicle details. Use when you need to extract license plate text, region information, or analyze vehicle attributes from images. | | `PLATERECOGNIZER_SNAPSHOT_GET_STATISTICS` | Snapshot Get Statistics | Tool to retrieve usage statistics for the current month's Snapshot API recognition calls. Use after making Snapshot API calls to monitor monthly usage. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Platerecognizer MCP server is an implementation of the Model Context Protocol that connects your AI agent to Platerecognizer. It provides structured and secure access so your agent can perform Platerecognizer 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 Platerecognizer - 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 Platerecognizer - MCPServerStreamableHTTP connects to the Platerecognizer 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 Platerecognizer 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 Platerecognizer composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["platerecognizer"], ) 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 Platerecognizer endpoint - The agent uses GPT-5 to interpret user commands and perform Platerecognizer operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent platerecognizer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[platerecognizer_mcp], instructions=( "You are a Platerecognizer assistant. Use Platerecognizer 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 - Platerecognizer 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 Platerecognizer.\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 Platerecognizer composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["platerecognizer"], ) 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 platerecognizer_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[platerecognizer_mcp], instructions=( "You are a Platerecognizer assistant. Use Platerecognizer 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 Platerecognizer.\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 Platerecognizer through Composio's Tool Router. With this setup, your agent can perform real Platerecognizer 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 + Platerecognizer 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 Platerecognizer MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/platerecognizer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/platerecognizer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/platerecognizer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/platerecognizer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/platerecognizer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/platerecognizer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/platerecognizer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/platerecognizer/framework/cli) - [Google ADK](https://composio.dev/toolkits/platerecognizer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/platerecognizer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/platerecognizer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/platerecognizer/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/platerecognizer/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/platerecognizer/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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Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Platerecognizer MCP? With a standalone Platerecognizer MCP server, the agents and LLMs can only access a fixed set of Platerecognizer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Platerecognizer 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 Platerecognizer tools. ### Can I manage the permissions and scopes for Platerecognizer while using Tool Router? Yes, absolutely. You can configure which Platerecognizer 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 Platerecognizer data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)