SIGN UPLOG IN
X (Twitter)LinkedInGitHubDiscordYouTube

How to integrate Foursquare MCP with CrewAI

Connect CrewAI to Foursquare MCP. Find coffee shops open near me now, show photos of central park attractions, and more using natural language, with authentication handled for you.

Get started for freeGet a demo
Foursquare logoFoursquare
Api Key

Foursquare is a global location platform for searching places and place recommendations. It helps users discover restaurants, shops, and venues with rich, up-to-date data.

8 Tools

Introduction

This guide walks you through connecting Foursquare to CrewAI using the Composio tool router. By the end, you'll have a working Foursquare agent that can search places, find nearby venues, autocomplete place names, fetch place photos and tips, and get trending venues through natural language commands.

This guide will help you understand how to give your CrewAI 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.

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Foursquare with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexPydantic AIAutoGen

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Foursquare connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Foursquare
  • Build a conversational loop where your agent can execute Foursquare 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 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.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Foursquare connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Foursquare via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

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
5

Import dependencies

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")
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 Foursquare MCP URL
6

Create a Composio Tool Router session for Foursquare

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["foursquare"])

url = session.mcp.url
What's happening:
  • You create a Foursquare only session through Composio
  • Composio returns an MCP HTTP URL that exposes Foursquare tools
7

Initialize the MCP Server

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,
    )
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.
8

Create a CLI Chatloop and define the Crew

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")
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.

Complete Code

Here's the complete code to get you started with Foursquare and CrewAI:

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=["foursquare"],
)
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 Foursquare through Composio's Tool Router. The agent can perform Foursquare 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
TOOLS

Supported Tools

Every Foursquare action and event your agent gets out of the box.

Autocomplete places

Returns autocomplete suggestions for places as the user types their search query.

Get trending venues

Searches for places around specified coordinates, sorted by popularity.

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.

Retrieve place photos by id

Retrieves photos associated with a specific place in Foursquare's database.

Retrieve places by id

Retrieves detailed information about a specific place using its unique Foursquare ID (FSQ ID).

Retrieve place tips using fsq id

Retrieves user-generated tips for a specific Foursquare place by its FSQ ID.

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.

Validate API credentials

Validates Foursquare API credentials by performing a lightweight place search.

FRAMEWORKS

How to build Foursquare MCP Agent with another framework

Claude Code logo

Claude Code

Use Foursquare MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Foursquare MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Foursquare MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Foursquare MCP with Claude Agent SDK

Codex logo

Codex

Use Foursquare MCP with Codex

OpenClaw logo

OpenClaw

Use Foursquare MCP with OpenClaw

Hermes logo

Hermes

Use Foursquare MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Foursquare MCP with Atomic Agent

Google ADK logo

Google ADK

Use Foursquare MCP with Google ADK

LangChain logo

LangChain

Use Foursquare MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Foursquare MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Foursquare MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Foursquare MCP with LlamaIndex

Pydantic AI logo

Pydantic AI

Use Foursquare MCP with Pydantic AI

AutoGen logo

AutoGen

Use Foursquare MCP with AutoGen

FAQ

Frequently asked questions

Start with Foursquare.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Foursquare tool your agent needs.Free to start.

Start building