# How to integrate Prerender MCP with Autogen ```json { "title": "How to integrate Prerender MCP with Autogen", "toolkit": "Prerender", "toolkit_slug": "prerender", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/prerender/framework/autogen", "markdown_url": "https://composio.dev/toolkits/prerender/framework/autogen.md", "updated_at": "2026-05-12T10:22:37.766Z" } ``` ## Introduction This guide walks you through connecting Prerender to AutoGen using the Composio tool router. By the end, you'll have a working Prerender agent that can fetch static html for your homepage, get prerendered content for blog post, retrieve seo snapshot for pricing page through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Prerender account through Composio's Prerender MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Prerender with - [OpenAI Agents SDK](https://composio.dev/toolkits/prerender/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/prerender/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/prerender/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/prerender/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/prerender/framework/codex) - [OpenClaw](https://composio.dev/toolkits/prerender/framework/openclaw) - [Hermes](https://composio.dev/toolkits/prerender/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/prerender/framework/cli) - [Google ADK](https://composio.dev/toolkits/prerender/framework/google-adk) - [LangChain](https://composio.dev/toolkits/prerender/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/prerender/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/prerender/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/prerender/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/prerender/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 Prerender - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Prerender tools - Run a live chat loop where you ask the agent to perform Prerender 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 Prerender MCP server, and what's possible with it? The Prerender MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Prerender account. It provides structured and secure access to your prerendered website snapshots, so your agent can fetch static HTML versions of your JavaScript-heavy pages, check SEO visibility, monitor crawler access, and automate site snapshot retrieval on your behalf. - Fetch prerendered HTML snapshots: Instantly retrieve static HTML versions of dynamic or JavaScript-rich pages for any given URL. - Automate SEO monitoring: Let your agent check how search engines will see your site by pulling the exact prerendered output served to crawlers. - Verify page rendering consistency: Use the agent to compare prerendered snapshots across multiple URLs or timeframes, keeping your content SEO-friendly and up to date. - Monitor site availability for crawlers: Have your agent ensure that key pages are accessible and properly rendered for search engines, alerting you to any issues. - Enable hands-free content auditing: Schedule or trigger regular snapshot fetches so your team can quickly audit site structure and indexed content—no manual browser tests required. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PRERENDER_CHANGE_RECACHE_SPEED` | Change Recache Speed | Tool to adjust the rendering speed of Manual/API and Automatic rendering queues. Use when you need to control how fast Prerender processes URLs in the recache queue. | | `PRERENDER_CHECK_HEALTH` | Check Health | Tool to check the health status of the Prerender API service. Use when you need to verify service availability or monitor system health. | | `PRERENDER_CLEAR_CACHE` | Clear Cache | Tool to clear Prerender cache using SQL-like wildcard patterns. Schedules a cache clear job. Only one cache clear job can be scheduled per user at a time. Use when you need to invalidate cached pages for a URL pattern. | | `PRERENDER_CREATE_SEO_AUDIT_REPORT` | Create SEO Audit Report | Tool to generate SEO audit reports for a specific URL. Use when you need to analyze SEO performance metrics for a web page. The report will be sent to the provided email address. | | `PRERENDER_GET_CACHE_CLEAR_STATUS` | Get Cache Clear Status | Tool to check the status of a cache clear job. Use when you need to verify if a cache clearing operation is still in progress or has completed. | | `PRERENDER_GET_PRERENDERED_PAGE` | Get Prerendered Page | Tool to fetch a prerendered HTML page. Use when you need a static snapshot of a page before dynamic rendering. | | `PRERENDER_LIST_HEALTHZ` | List Healthz | Tool to check Prerender API health and availability. Use when you need to verify the API is operational before making requests. | | `PRERENDER_LIST_V3` | List V3 | Tool to get a greeting message from the Prerender API. Use when you need to verify basic API connectivity or retrieve the hello message. | | `PRERENDER_RECACHE_URL` | Recache URL | Tool to cache or recache URLs in Prerender. Use when you need to refresh cached pages or cache new URLs. Supports single URL or batch operations (up to 1000 URLs). | | `PRERENDER_SEARCH_CACHED_URLS` | Search Cached URLs | Tool to search for cached URLs in your Prerender account and view their cache status. Supports pagination (200 URLs per page) and filtering by desktop/mobile adaptive type. Use when you need to find specific cached pages or check cache status across your account. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Prerender MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Prerender. Instead of manually wiring Prerender 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 Prerender 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 Prerender 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 Prerender 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 Prerender 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 Prerender session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["prerender"] ) 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 Prerender 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 Prerender assistant agent with MCP tools agent = AssistantAgent( name="prerender_assistant", description="An AI assistant that helps with Prerender 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 Prerender 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 Prerender 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 Prerender session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["prerender"] ) 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 Prerender assistant agent with MCP tools agent = AssistantAgent( name="prerender_assistant", description="An AI assistant that helps with Prerender 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 Prerender 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 Prerender 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 Prerender, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Prerender MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/prerender/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/prerender/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/prerender/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/prerender/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/prerender/framework/codex) - [OpenClaw](https://composio.dev/toolkits/prerender/framework/openclaw) - [Hermes](https://composio.dev/toolkits/prerender/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/prerender/framework/cli) - [Google ADK](https://composio.dev/toolkits/prerender/framework/google-adk) - [LangChain](https://composio.dev/toolkits/prerender/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/prerender/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/prerender/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/prerender/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/prerender/framework/crew-ai) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences. - [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses. - [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure. - [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure. - [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction. - [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting. - [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. - [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Prerender MCP? With a standalone Prerender MCP server, the agents and LLMs can only access a fixed set of Prerender tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Prerender 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 Prerender tools. ### Can I manage the permissions and scopes for Prerender while using Tool Router? Yes, absolutely. You can configure which Prerender 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 Prerender data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)