MCP IP Geolocation Server MCP Server: Powering Model Context Protocol Integration

Overview: What is MCP IP Geolocation Server?

MCP (Model Context Protocol) is a universal adapter designed to integrate various data sources and tools into AI applications. This server acts as an intermediary, enabling seamless communication between Model Context Protocol clients like Claude Desktop, Continue, Cursor, and other advanced AI tools with external services such as geolocation databases provided by IP-API.com.

🔧 Core Features & MCP Capabilities

This MCP server leverages the power of Model Context Protocol to deliver real-time, accurate location information for any IP address. The underlying geolocation data is sourced from IP-API.com using their free tier, which does not require an API key or registration. By implementing the MCP protocol, this server ensures that AI applications can easily and efficiently request location details without manual setup.

The core features of this server include:

Detailed Location Information: Get comprehensive geographic data including city, region, country, latitude, longitude, timezone, ISP, AS number, and more.
Clean, Formatted Output: The structured output is designed to be easy for AI applications such as Claude Desktop to parse and utilize in a natural language interface.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the MCP IP Geolocation Server is built around the Model Context Protocol framework. This server adheres strictly to the MCP standards, allowing it to seamlessly integrate with various AI clients while ensuring protocol consistency across multiple services.

MCP Client Compatibility Matrix

Our compatibility matrix highlights which AI applications can fully leverage this MCP server:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	❌	Partial Functionality
Cursor	❌	✅	❌	Tools Only

This matrix demonstrates that the server has full support for resources and tools in Claude Desktop while providing limited compatibility with prompts for certain clients.

MCP Protocol Flow Diagram

Understanding the flow of data through the Model Context Protocol, we have visualized it as follows:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates how MCP clients interact with the server to retrieve geolocation data.

🚀 Getting Started with Installation

To get started using this MCP IP Geolocation Server, follow these steps:

Install Globally:
```
npm install -g mcp-ip-geolocator
```
Run the Server:
```
mcp-ip-geolocator
```

For local development and more advanced setups, the following guide is provided.

💡 Key Use Cases in AI Workflows

Scenario 1: Enhancing Cybersecurity Monitoring

Imagine an enterprise using Claude Desktop for cybersecurity monitoring. With this MCP server installed, it can quickly query geolocation data to identify suspicious activities or unauthorized access attempts based on IP addresses. This integration enhances the ability of AI tools to contextualize and analyze network traffic effectively.

Scenario 2: Geographical Targeting in Marketing Campaigns

A marketing team using Continue could utilize the geolocation capabilities provided by this MCP server to target specific geographic regions. For instance, they can send personalized promotions or sales emails based on user IP addresses, ensuring that content is relevant to their audience's location and preferences.

🔌 Integration with MCP Clients

When integrating this MCP IP Geolocation Server into an AI workflow, it acts as a bridge between the client application (like Claude Desktop) and external geolocation services. This setup ensures that data requests are consistent across different clients while maintaining the integrity of network protocols.

Here’s a sample configuration for the MCP server in your AI tool setup:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

The server adheres to IP-API.com's free tier limitations, which include:

45 Requests Per Minute: With both IPv4 and IPv6 support provided.
No API Key Needed: Access is straightforward without additional security measures.

Real-Time Data Flow

This chart provides a high-level view of the data flow within the server:

graph TD;
    A[IP Address Request] --> B[MCP Protocol]
    B --> C[Server]
    C --> D[Data from IP-API.com]
    D --> E[Processed Geolocation Data]
    E --> F[AI Application Response]

🛠️ Advanced Configuration & Security

Advanced users may wish to configure the server to meet specific security needs or operational requirements. The server supports environment variable settings that can be adjusted for different deployment scenarios.

For example, setting up an API key requires modifying the env block in the configuration file:

"env": {
  "API_KEY": "<insert-your-api-key>"
}

❓ Frequently Asked Questions (FAQ)

Q1: Is there a limit to the number of requests I can send per minute?

A1: Yes, IP-API.com's free tier allows for up to 45 requests per minute.

Q2: Can I use this server with other MCP clients besides Claude Desktop?

A2: The compatibility matrix shows that certain clients have partial or full support. Check the matrix before integrating.

Q3: How do I set up an API key, and where should I place it in the configuration file?

A3: Place your API key in the env block:

"env": {
  "API_KEY": "<insert-your-api-key>"
}

Q4: Are IPv6 addresses supported by this server?

A4: Yes, this server supports both IPv4 and IPv6 requests.

Q5: What happens if I exceed the minute limit for API requests?

A5: While IP-API.com includes a generous free tier, exceeding limits may result in rate limiting or temporary service interruptions.

👨‍💻 Development & Contribution Guidelines

Contributions to this project are welcomed! Developers who wish to contribute should adhere to these guidelines:

Fork the repository
Create a feature branch
Ensure tests pass with yarn test
Submit pull requests for review

🌐 MCP Ecosystem & Resources

The Model Context Protocol ecosystem includes various tools and resources designed to facilitate integration between different AI applications. This geolocation server is part of an expanding network of adapters that enable seamless data flow and enhance the functionality of AI tools.

For more information, visit the official Model Context Protocol documentation: https://modelcontextprotocol.org/documentatio

By integrating this MCP IP Geolocation Server with your AI application, you can significantly increase its capabilities in handling location-based data, providing a robust foundation for advanced workflows.

MCP IP Geolocation Server