# How to integrate Zenserp MCP with Pydantic AI ```json { "title": "How to integrate Zenserp MCP with Pydantic AI", "toolkit": "Zenserp", "toolkit_slug": "zenserp", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/zenserp/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/zenserp/framework/pydantic-ai.md", "updated_at": "2026-05-06T08:34:37.106Z" } ``` ## Introduction This guide walks you through connecting Zenserp to Pydantic AI using the Composio tool router. By the end, you'll have a working Zenserp agent that can find top news articles on ai ethics, get trending keywords for electric cars, list local coffee shops in brooklyn through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Zenserp account through Composio's Zenserp MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Zenserp with - [OpenAI Agents SDK](https://composio.dev/toolkits/zenserp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zenserp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zenserp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zenserp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zenserp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zenserp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zenserp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zenserp/framework/cli) - [Google ADK](https://composio.dev/toolkits/zenserp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zenserp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zenserp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zenserp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zenserp/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zenserp/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 Zenserp - 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 Zenserp 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 Zenserp MCP server, and what's possible with it? The Zenserp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zenserp account. It provides structured and secure access to real-time search engine results, so your agent can perform actions like running Google searches, grabbing news headlines, pulling images, analyzing trends, and even fetching local business data on your behalf. - Comprehensive Google and Bing search: Instantly run structured web searches and retrieve up-to-date SERP data from Google or Bing for any query. - Automated news and trend analysis: Have your agent fetch recent Google News articles or analyze keyword popularity over time using Google Trends data. - Reverse image and visual content search: Perform reverse image lookups or image searches to discover where an image appears online or find relevant pictures for any topic. - Shopping and video discovery: Search Google Shopping for product offers or Google Video for relevant multimedia results, all via agent-driven queries. - Local and map-based business lookup: Let your agent use Google Maps search to find businesses or places based on location, keywords, or coordinates for local intelligence. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ZENSERP_BING_SEARCH` | Bing Search | Tool to obtain bing search results. use when you need real-time scraping of bing serps from bing.com. | | `ZENSERP_GOOGLE_NEWS_SEARCH` | Google News Search | Tool to perform a google news search. use when you need recent news articles for a topic. example: "search news for climate change". | | `ZENSERP_GOOGLE_REVERSE_IMAGE_SEARCH` | Google Reverse Image Search | Tool to perform a reverse image search on google. use after obtaining a public image url to find where the image appears online. | | `ZENSERP_GOOGLE_SHOPPING_SEARCH` | Google Shopping Search | Tool to perform a google shopping search. use when you need structured product offers and pricing data via zenserp api. | | `ZENSERP_GOOGLE_TRENDS` | Google Trends | Tool to retrieve google trends data. use when comparing keyword popularity over time. | | `ZENSERP_GOOGLE_VIDEO_SEARCH` | Google Video Search | Tool to perform a google video search via zenserp. use when you need video-specific search results. | | `ZENSERP_YANDEX_SEARCH` | Yandex Search via Zenserp | Tool to obtain yandex search results via zenserp api. use when you need programmatic access to yandex search data after constructing a query. | | `ZENSERP_ZENSERP_GOOGLE_IMAGE_SEARCH` | Zenserp Google Image Search | Tool to perform a google image search via zenserp. use when you need structured image search results for a specific query. | | `ZENSERP_ZENSERP_GOOGLE_MAPS_SEARCH` | Google Maps Search | Tool to perform a google maps (local) search. use when you need localized business results for a given query. provide 'location' or 'lat'/'lng' for geotargeting. | | `ZENSERP_ZENSERP_GOOGLE_SEARCH` | Zenserp Google Search | Tool to perform a standard google search via zenserp. use when you need structured serp data for a given query. | | `ZENSERP_GOOGLE_SHOPPING_SEARCH` | Google Shopping Search | Tool to perform a google shopping search. use when you need structured product offers and pricing data via zenserp api. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Zenserp MCP server is an implementation of the Model Context Protocol that connects your AI agent to Zenserp. It provides structured and secure access so your agent can perform Zenserp 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 Zenserp - 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 Zenserp - MCPServerStreamableHTTP connects to the Zenserp 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 Zenserp 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 Zenserp composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["zenserp"], ) 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 Zenserp endpoint - The agent uses GPT-5 to interpret user commands and perform Zenserp operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent zenserp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[zenserp_mcp], instructions=( "You are a Zenserp assistant. Use Zenserp 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 - Zenserp 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 Zenserp.\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 Zenserp composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["zenserp"], ) 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 zenserp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[zenserp_mcp], instructions=( "You are a Zenserp assistant. Use Zenserp 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 Zenserp.\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 Zenserp through Composio's Tool Router. With this setup, your agent can perform real Zenserp 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 + Zenserp 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 Zenserp MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/zenserp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zenserp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zenserp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zenserp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zenserp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zenserp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zenserp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zenserp/framework/cli) - [Google ADK](https://composio.dev/toolkits/zenserp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zenserp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zenserp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zenserp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zenserp/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zenserp/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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Get detailed environmental insights directly from your own sensors for smarter apps and automations. - [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. 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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 Zenserp MCP? With a standalone Zenserp MCP server, the agents and LLMs can only access a fixed set of Zenserp tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zenserp 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 Zenserp tools. ### Can I manage the permissions and scopes for Zenserp while using Tool Router? Yes, absolutely. You can configure which Zenserp 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 Zenserp data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)