# How to integrate Foursquare MCP with Pydantic AI ```json { "title": "How to integrate Foursquare MCP with Pydantic AI", "toolkit": "Foursquare", "toolkit_slug": "foursquare", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/foursquare/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/foursquare/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:12:14.885Z" } ``` ## Introduction This guide walks you through connecting Foursquare to Pydantic AI using the Composio tool router. By the end, you'll have a working Foursquare agent that can find coffee shops open near me now, show photos of central park attractions, get reviews for the best pizza spots nearby through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Foursquare account through Composio's Foursquare MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Foursquare with - [OpenAI Agents SDK](https://composio.dev/toolkits/foursquare/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/foursquare/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/foursquare/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/foursquare/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/foursquare/framework/codex) - [OpenClaw](https://composio.dev/toolkits/foursquare/framework/openclaw) - [Hermes](https://composio.dev/toolkits/foursquare/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/foursquare/framework/cli) - [Google ADK](https://composio.dev/toolkits/foursquare/framework/google-adk) - [LangChain](https://composio.dev/toolkits/foursquare/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/foursquare/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/foursquare/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/foursquare/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/foursquare/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 Foursquare - 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 Foursquare 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 Foursquare MCP server, and what's possible with it? The Foursquare MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Foursquare account. It provides structured and secure access to the powerful Foursquare Places database, so your agent can search for venues, recommend places, retrieve detailed location data, surface user tips, and even fetch photos—all on your behalf. - Comprehensive place search and discovery: Let your agent find places or points of interest nearby or in any area using keywords, categories, or specific criteria. - Retrieve rich place details: Instantly pull in-depth information about a specific venue, including its name, address, ratings, categories, and more. - Access user-generated tips and reviews: Have your agent surface real user insights, tips, and experiences for any place to help guide your decisions. - Fetch location photos: Enhance your applications by retrieving and displaying user-contributed images for any venue in the Foursquare database. - Explore lesser-known and trending spots: Use Foursquare's broader search to discover new, up-and-coming, or hidden places that might not appear in standard searches. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FOURSQUARE_RETRIEVE_NEARBY_PLACES_V3` | Retrieve nearby places v3 | The GetNearbyPlaces endpoint retrieves a list of places near a specified location, primarily supporting check-in use cases and local discovery. It returns points of interest (POIs) including lower quality results not found in the standard Place Search, enhancing location-based experiences with additional data like photos, reviews, and tips. This endpoint is ideal for applications seeking to provide users with a comprehensive view of their surroundings, including less prominent or newer locations. While it offers a broader range of results, it may sacrifice some precision compared to more focused search endpoints. Use this when you want to offer users a diverse array of nearby options, particularly for social check-in features or exploratory local recommendations. | | `FOURSQUARE_RETRIEVE_PLACE_PHOTOS_BY_ID` | Retrieve place photos by id | Retrieves photos associated with a specific place in Foursquare's database. This endpoint allows you to access user-generated images for a particular point of interest (POI) using its unique Foursquare ID (fsq_id). It's useful for enhancing your application with visual content related to locations, such as restaurants, landmarks, or businesses. The endpoint returns photo data that can be used to construct image URLs for display. Keep in mind that the number and quality of photos may vary depending on the popularity and user engagement of the place. This tool should be used when you need to display or analyze visual information about a specific location in your application. | | `FOURSQUARE_RETRIEVE_PLACES_BY_ID` | Retrieve places by id | Retrieves detailed information about a specific place using its unique Foursquare ID (FSQ ID). This endpoint provides comprehensive data about a venue, including its name, address, category, ratings, tips, photos, and other relevant information. It's particularly useful when you need in-depth details about a known location, such as for displaying venue profiles or gathering specific place attributes. The endpoint should be used when you have a valid FSQ ID and require the most up-to-date and complete information about that place. Note that this endpoint focuses on individual place details and does not provide search functionality or lists of multiple venues. | | `FOURSQUARE_RETRIEVE_PLACE_TIPS_USING_FSQ_ID` | Retrieve place tips using fsq id | Retrieves user-generated tips for a specific place in the Foursquare database. This endpoint allows you to fetch valuable insights and experiences shared by Foursquare users about a particular venue. It's useful for enhancing location-based applications with real user feedback, helping users make informed decisions about places they might visit. The endpoint returns a list of tips, which may include information such as the tip text, the user who created it, and potentially a timestamp or rating. | | `FOURSQUARE_SEARCH_PLACES_API_REQUEST` | Search places api request | The GetPlacesSearch endpoint allows you to search for places in the Foursquare database based on various criteria such as location, keywords, and categories. This tool is ideal for discovering nearby points of interest or finding specific venues. It returns a list of places matching the specified parameters, providing essential information about each location. Use this endpoint for location-based features or gathering venue information in a specific area. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Foursquare MCP server is an implementation of the Model Context Protocol that connects your AI agent to Foursquare. It provides structured and secure access so your agent can perform Foursquare 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 Foursquare - 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 Foursquare - MCPServerStreamableHTTP connects to the Foursquare 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 Foursquare 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 Foursquare composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["foursquare"], ) 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 Foursquare endpoint - The agent uses GPT-5 to interpret user commands and perform Foursquare operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent foursquare_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[foursquare_mcp], instructions=( "You are a Foursquare assistant. Use Foursquare 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 - Foursquare 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 Foursquare.\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 Foursquare composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["foursquare"], ) 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 foursquare_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[foursquare_mcp], instructions=( "You are a Foursquare assistant. Use Foursquare 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 Foursquare.\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 Foursquare through Composio's Tool Router. With this setup, your agent can perform real Foursquare 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 + Foursquare 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 Foursquare MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/foursquare/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/foursquare/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/foursquare/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/foursquare/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/foursquare/framework/codex) - [OpenClaw](https://composio.dev/toolkits/foursquare/framework/openclaw) - [Hermes](https://composio.dev/toolkits/foursquare/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/foursquare/framework/cli) - [Google ADK](https://composio.dev/toolkits/foursquare/framework/google-adk) - [LangChain](https://composio.dev/toolkits/foursquare/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/foursquare/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/foursquare/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/foursquare/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/foursquare/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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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. - [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Foursquare MCP? With a standalone Foursquare MCP server, the agents and LLMs can only access a fixed set of Foursquare tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Foursquare 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 Foursquare tools. ### Can I manage the permissions and scopes for Foursquare while using Tool Router? Yes, absolutely. You can configure which Foursquare 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 Foursquare data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)