# How to integrate Yelp MCP with Pydantic AI ```json { "title": "How to integrate Yelp MCP with Pydantic AI", "toolkit": "Yelp", "toolkit_slug": "yelp", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/yelp/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/yelp/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:30:54.821Z" } ``` ## Introduction This guide walks you through connecting Yelp to Pydantic AI 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 Pydantic AI 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: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Yelp - 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 Yelp 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 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 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 Yelp - 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 Yelp - MCPServerStreamableHTTP connects to the Yelp 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 Yelp 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 Yelp composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["yelp"], ) 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 Yelp endpoint - The agent uses GPT-5 to interpret user commands and perform Yelp operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent yelp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[yelp_mcp], instructions=( "You are a Yelp assistant. Use Yelp 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 - Yelp 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 Yelp.\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 Yelp composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["yelp"], ) 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 yelp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[yelp_mcp], instructions=( "You are a Yelp assistant. Use Yelp 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 Yelp.\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 Yelp through Composio's Tool Router. With this setup, your agent can perform real Yelp 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 + Yelp 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 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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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 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 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)