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NearbySearch MCP Server: Instant IP Geolocation & Scalable Precision - MCP Implementation

NearbySearch MCP Server: Instant IP Geolocation & Scalable Precision

Pinpoint nearby locations instantly with IP-based geolocation for businesses, apps, and beyond—fast, accurate, and built for scale.

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About NearbySearch MCP Server

What is NearbySearch MCP Server: Instant IP Geolocation & Scalable Precision?

This server enables real-time geolocation-based searches by integrating IP detection with Google Places API. Designed for developers and enterprises, it delivers precise location-aware results through automated IP geolocation and customizable search parameters, ensuring seamless integration into applications requiring dynamic spatial analysis.

How to Use NearbySearch MCP Server: Instant IP Geolocation & Scalable Precision?

  1. Setup Environment: Install Python 3.10+ and configure Google Cloud API credentials with Places API access.
  2. Deploy Server: Use MCP CLI tools to run in development mode with mcp dev main.py or deploy via mcp install for production workflows.
  3. Execute Queries: Invoke the search_nearby endpoint with parameters like keyword, radius, and type to retrieve location-specific results.

NearbySearch MCP Server Features

Key Features of NearbySearch MCP Server: Instant IP Geolocation & Scalable Precision?

  • Instant IP geolocation for real-time location detection using the ipapi.co API.
  • Granular control over search parameters: adjust radius (default 1500m), specify place types (e.g., "hospital", "bank"), and filter by keywords.
  • Scalable architecture designed for high-traffic applications with built-in rate limiting via Google API credentials.

Use Cases of NearbySearch MCP Server: Instant IP Geolocation & Scalable Precision?

Optimize spatial workflows in:

  • On-demand services: Real-time driver dispatch systems using client IP-derived locations.
  • Retail analytics: Store locator features with radius-based inventory visibility.
  • Emergency response: Proximity-based resource allocation using geofenced search parameters.

NearbySearch MCP Server FAQ

FAQ: NearbySearch MCP Server Operational Insights

  • Q: How does IP geolocation accuracy compare to GPS?
    A: Provides city-level precision (±5km), ideal for non-critical spatial applications.
  • Q: Can the server handle concurrent requests?
    A: Yes, MCP orchestration ensures parallel processing within Google API quota limits.
  • Q: What's the recommended deployment architecture?
    A: Containerized via Docker for consistent environments across dev/production tiers.

Content

NearbySearch MCP Server

An MCP server for nearby place searches with IP-based location detection.

GitHub License GitHub Last Commit Python Version

Features

  • IP-based Location Detection : Uses ipapi.co to determine your current location
  • Google Places Integration : Searches for nearby places based on keywords and optional type filters
  • Simple Interface : Single tool endpoint with customizable radius

Requirements

  • Python 3.10+
  • Google Cloud Platform API Key with Places API enabled
  • Internet connection

Installation

  1. Clone the repository:
git clone https://github.com/kukapay/nearby-search-mcp.git
cd nearby-search-mcp
  1. Install dependencies:
# Using uv (recommended)
uv add "mcp[cli]" httpx python-dotenv

# Or using pip
pip install mcp httpx python-dotenv
  1. Client Configuration
{
  "mcpServers": {
    "nearby-search": {
      "command": "uv",
      "args": ["--directory", "path/to/nearby-search-mcp", "run", "main.py"],
      "env": {
        "GOOGLE_API_KEY": "your google api key"
      }
    }
  }
}

Usage

Running the Server

  • Development Mode (with MCP Inspector):

    mcp dev main.py

  • Install in Claude Desktop :

    mcp install main.py --name "NearbySearch"

  • Direct Execution :

    python main.py

Available Endpoints

Tool:search_nearby

  • Searches for places near your current location
  • Parameters:
    • keyword (str): What to search for (e.g., "coffee shop")
    • radius (int, optional): Search radius in meters (default: 1500)
    • type (str, optional): Place type (e.g., "restaurant", "cafe")

License

This project is licensed under the MIT License - see the LICENSE file for details.

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