# How to integrate Zenserp MCP with Autogen ```json { "title": "How to integrate Zenserp MCP with Autogen", "toolkit": "Zenserp", "toolkit_slug": "zenserp", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/zenserp/framework/autogen", "markdown_url": "https://composio.dev/toolkits/zenserp/framework/autogen.md", "updated_at": "2026-05-06T08:34:37.106Z" } ``` ## Introduction This guide walks you through connecting Zenserp to AutoGen 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 AutoGen 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: - Get and set up your OpenAI and Composio API keys - Install the required dependencies for Autogen and Composio - Initialize Composio and create a Tool Router session for Zenserp - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Zenserp tools - Run a live chat loop where you ask the agent to perform Zenserp operations ## What is AutoGen? Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows. Key features include: - Multi-Agent Systems: Build collaborative agent workflows - MCP Workbench: Native support for Model Context Protocol tools - Streaming HTTP: Connect to external services through streamable HTTP - AssistantAgent: Pre-built agent class for tool-using assistants ## 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 agents and assistants directly to Zenserp. Instead of manually wiring Zenserp APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. 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 You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Zenserp account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 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 Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Zenserp via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables Create a .env file in your project folder. What's happening: - COMPOSIO_API_KEY is required to talk to Composio - OPENAI_API_KEY is used by Autogen's OpenAI client - USER_ID is how Composio identifies which user's Zenserp connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Zenserp tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Zenserp session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["zenserp"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for. What's happening: - url points to the Tool Router MCP endpoint from Composio - timeout is the HTTP timeout for requests - sse_read_timeout controls how long to wait when streaming responses - terminate_on_close=True cleans up the MCP server process when the workbench is closed ```python # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Zenserp tools from the workbench ```python # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Zenserp assistant agent with MCP tools agent = AssistantAgent( name="zenserp_assistant", description="An AI assistant that helps with Zenserp operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Zenserp tools to call via MCP - agent.run_stream(...) yields streaming messages as the agent thinks and calls tools - Typing exit, quit, or bye ends the loop ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Zenserp related question or task to the agent.\n") # Conversation loop 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") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, "content") and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Zenserp session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["zenserp"] ) url = session.mcp.url # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Zenserp assistant agent with MCP tools agent = AssistantAgent( name="zenserp_assistant", description="An AI assistant that helps with Zenserp operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Zenserp related question or task to the agent.\n") # Conversation loop 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") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, 'content') and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You now have an Autogen assistant wired into Zenserp through Composio's Tool Router and MCP. From here you can: - Add more toolkits to the toolkits list, for example notion or hubspot - Refine the agent description to point it at specific workflows - Wrap this script behind a UI, Slack bot, or internal tool Once the pattern is clear for Zenserp, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## 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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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 Autogen? Yes, you can. Autogen 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)