# How to integrate Yelp MCP with Autogen ```json { "title": "How to integrate Yelp MCP with Autogen", "toolkit": "Yelp", "toolkit_slug": "yelp", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/yelp/framework/autogen", "markdown_url": "https://composio.dev/toolkits/yelp/framework/autogen.md", "updated_at": "2026-05-12T10:30:54.821Z" } ``` ## Introduction This guide walks you through connecting Yelp to AutoGen using the Composio tool router. By the end, you'll have a working Yelp agent that can find top-rated coffee shops nearby, show best pizza places open now, list vegan restaurants within 2 miles through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Yelp account through Composio's Yelp MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Yelp with - [OpenAI Agents SDK](https://composio.dev/toolkits/yelp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/yelp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/yelp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/yelp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/yelp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/yelp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/yelp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/yelp/framework/cli) - [Google ADK](https://composio.dev/toolkits/yelp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/yelp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/yelp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/yelp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/yelp/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/yelp/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 Yelp - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Yelp tools - Run a live chat loop where you ask the agent to perform Yelp 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 Yelp MCP server, and what's possible with it? The Yelp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and more directly to Yelp's extensive business data. It provides structured and secure access to business search, reviews, ratings, and local business details, so your agent can help you find businesses, compare ratings, read reviews, and discover local favorites on your behalf. - Business discovery and search: Ask your agent to find restaurants, shops, or services by location, category, or specific business name with up-to-date Yelp data. - Detailed review retrieval: Have your agent fetch and summarize customer reviews for any business, making it easier to choose where to go. - Ratings and reputation checks: Let your agent provide business ratings, number of reviews, and popularity insights before you make a decision. - Local business information access: Get detailed information like address, hours, contact info, and amenities for businesses near you or in any city. - Personalized recommendations: Enable your agent to suggest top-rated options based on your preferences, trending spots, or special occasions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `YELP_GET_BUSINESS_DETAILS` | Get Business Details | Get detailed information about a specific business on Yelp using its business ID or alias. Returns comprehensive business information including hours (in the business's local timezone), photos, reviews, and location details. The returned `url` field is the Yelp listing page, not the business's own website. Response fields such as `phone` and `website` may be null; handle missing values explicitly. Avoid many parallel calls — HTTP 429 throttling applies; limit concurrency to ~5 parallel requests with exponential backoff. | | `YELP_GET_BUSINESS_REVIEWS` | Get Business Reviews | Get reviews for a specific business on Yelp using its business ID or alias. Returns up to 3 review excerpts for the business. | | `YELP_GET_REVIEW_HIGHLIGHTS` | Get Review Highlights | Get review highlights for a specific business on Yelp using its business ID or alias. Returns summarized key points and themes from customer reviews. IMPORTANT: This endpoint requires Yelp Places API Premium Plan access. Without Premium Plan, requests will return a 403 NOT_AUTHORIZED error. For basic review access, consider using the Get Business Reviews action instead, which is available on Enhanced and Premium plans. Note: Get Business Reviews returns at most 3 recent reviews per call, while this action synthesizes themes across the full review history. | | `YELP_SEARCH_AND_CHAT` | Search and Chat | Chat with Yelp's AI assistant to search for businesses, get recommendations, and ask questions. This action provides a conversational interface to Yelp's AI that can: - Search for businesses by type, location, and criteria (e.g., "best Italian restaurants near Times Square") - Answer questions about specific businesses (e.g., "what are the hours for The Purple Pig?") - Provide recommendations based on user preferences - Maintain conversation context when chat_id is provided for follow-up questions The response includes the AI's natural language answer along with detailed business data including ratings, reviews, locations, photos, and attributes for any mentioned businesses. | | `YELP_SEARCH_BUSINESSES` | Search Businesses | Search for businesses on Yelp by location, term, categories, and other filters. Returns at most 50 results per call; use offset to paginate. Overly restrictive filter combinations (categories, price, radius) can yield zero results — loosen iteratively. Results may include businesses from adjacent areas; post-process on location.city or distance for strict boundaries. The returned url field is the Yelp listing page, not the business's own website. Rapid parallel calls can trigger HTTP 429 — apply exponential backoff. | | `YELP_SEARCH_BY_PHONE` | Search Business by Phone | Search for a business by phone number on Yelp. Returns business data including business_id, required by YELP_GET_BUSINESS_DETAILS, YELP_GET_BUSINESS_REVIEWS, and YELP_GET_REVIEW_HIGHLIGHTS. Empty results are inconclusive due to incomplete Yelp coverage. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Yelp MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Yelp. Instead of manually wiring Yelp 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 Yelp 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 Yelp 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 Yelp 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 Yelp 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 Yelp session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["yelp"] ) 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 Yelp 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 Yelp assistant agent with MCP tools agent = AssistantAgent( name="yelp_assistant", description="An AI assistant that helps with Yelp 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 Yelp 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 Yelp 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 Yelp session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["yelp"] ) 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 Yelp assistant agent with MCP tools agent = AssistantAgent( name="yelp_assistant", description="An AI assistant that helps with Yelp 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 Yelp 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 Yelp 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 Yelp, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Yelp MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/yelp/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/yelp/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/yelp/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/yelp/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/yelp/framework/codex) - [OpenClaw](https://composio.dev/toolkits/yelp/framework/openclaw) - [Hermes](https://composio.dev/toolkits/yelp/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/yelp/framework/cli) - [Google ADK](https://composio.dev/toolkits/yelp/framework/google-adk) - [LangChain](https://composio.dev/toolkits/yelp/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/yelp/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/yelp/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/yelp/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/yelp/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 Yelp MCP? With a standalone Yelp MCP server, the agents and LLMs can only access a fixed set of Yelp tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Yelp 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 Yelp tools. ### Can I manage the permissions and scopes for Yelp while using Tool Router? Yes, absolutely. You can configure which Yelp 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 Yelp data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)