# How to integrate Yelp MCP with CrewAI ```json { "title": "How to integrate Yelp MCP with CrewAI", "toolkit": "Yelp", "toolkit_slug": "yelp", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/yelp/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/yelp/framework/crew-ai.md", "updated_at": "2026-05-12T10:30:54.821Z" } ``` ## Introduction This guide walks you through connecting Yelp to CrewAI 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 CrewAI 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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Yelp connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Yelp - Build a conversational loop where your agent can execute Yelp operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## 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 agent to Yelp. It provides structured and secure access so your agent can perform Yelp 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 and API key - A Yelp connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Yelp via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Yelp MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Yelp **What's happening:** - You create a Yelp only session through Composio - Composio returns an MCP HTTP URL that exposes Yelp tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["yelp"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["yelp"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Yelp through Composio's Tool Router. The agent can perform Yelp operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## 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) ## 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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It helps streamline how you gather, organize, and analyze responses. ## 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 CrewAI? Yes, you can. CrewAI 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)