Foursquare Places MCP Server

What does “Foursquare Places” MCP Server do?

The Foursquare Places MCP Server is a Model Context Protocol (MCP) implementation that connects AI assistants to the Foursquare Places API, enabling them to access rich, real-time location data. By interfacing with Foursquare’s global database of over 100 million places across 1500+ categories, this server empowers AI applications to perform advanced local searches, geotagging, and contextual awareness tasks. Developers can leverage this tool to enable AI agents to retrieve detailed metadata—including reviews, ratings, photos, and popularity metrics—for locations near a user or within specified parameters. This integration allows for situationally aware AI agents and applications that can provide highly personalized, location-based recommendations and insights.

List of Prompts

No information about prompt templates was found in the repository.

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List of Resources

No explicit list of MCP resources is described in the repository documentation.

List of Tools

No direct listing of tools (e.g., tool definitions in server.py or similar) could be found based on the available documentation and files.

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Use Cases of this MCP Server

• Local Place Search: Enables AI agents to search for nearby places using Foursquare’s extensive location database, providing users with contextually relevant recommendations.
• Geotagging and Place Snap: Utilizes Place Snap technology to accurately pinpoint user locations and match them to real-world venues, enhancing navigation and check-in experiences.
• Contextual Metadata Retrieval: Allows retrieval of rich metadata for places—including reviews, ratings, photos, and popularity—enabling AI agents to deliver detailed information to users.
• Personalized Experience: Facilitates the creation of situationally aware AI agents that tailor responses and suggestions based on a user’s current location and preferences.
• Location-based Insights: Supports applications that need to convert raw GPS data into actionable insights, such as identifying popular venues, places of interest, or business intelligence.

How to set it up

Windsurf

1. Ensure you have Python and Node.js installed.
2. Obtain your Foursquare Service API Key (see Foursquare developer documentation).
3. Edit the Windsurf configuration file (e.g., windsurf.config.json).
4. Add the Foursquare Places MCP server using a JSON snippet:

   {
     "mcpServers": {
       "foursquare-places": {
         "command": "python",
         "args": ["-m", "fsq-server-python.server"]
       }
     }
   }
   

5. Save the configuration and restart Windsurf.
6. Verify setup by checking the MCP server status in the Windsurf interface.

Claude

1. Download and install the Claude Desktop App .
2. Obtain your Foursquare Service API Key.
3. Follow the instructions in fsq-server-python/README.md to set up the MCP server locally.
4. In the Claude Desktop App, access the configuration panel and add:

   {
     "mcpServers": {
       "foursquare-places": {
         "command": "python",
         "args": ["-m", "fsq-server-python.server"]
       }
     }
   }
   

5. Save and restart Claude Desktop. Confirm the server is running via the MCP servers list.

Cursor

1. Install Python and ensure Node.js is available.
2. Obtain your Foursquare API key.
3. Open Cursor’s configuration file.
4. Add the following MCP server entry:

   {
     "mcpServers": {
       "foursquare-places": {
         "command": "python",
         "args": ["-m", "fsq-server-python.server"]
       }
     }
   }
   

5. Save changes, restart Cursor, and verify connection.

Cline

1. Ensure Python and Node.js are installed.
2. Obtain your Foursquare API Key.
3. Edit the Cline MCP server configuration.
4. Insert:

   {
     "mcpServers": {
       "foursquare-places": {
         "command": "python",
         "args": ["-m", "fsq-server-python.server"]
       }
     }
   }
   

5. Save configuration and restart Cline; verify the MCP server is listed.

Securing API Keys

• Store your Foursquare API key in an environment variable (e.g., FSQ_API_KEY).
• Example configuration with environment variable:

  {
    "mcpServers": {
      "foursquare-places": {
        "command": "python",
        "args": ["-m", "fsq-server-python.server"],
        "env": {
          "FSQ_API_KEY": "${FSQ_API_KEY}"
        },
        "inputs": {
          "api_key": "${FSQ_API_KEY}"
        }
      }
    }
  }
  

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "foursquare-places": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “foursquare-places” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the available documentation, the Foursquare Places MCP server provides a solid overview and setup instructions, but lacks explicit details on prompts, resources, tools, roots, and sampling support. The project is at an early stage and documentation is minimal beyond setup.

Our opinion

Given the limited information and missing details on key MCP concepts (such as tools and resources), this MCP server scores a 3/10. It has a clear purpose and setup instructions, but lacks depth in its MCP integration documentation.

MCP Score