IP Geolocation MCP Server

Overview

The IP Geolocation MCP Server bridges the gap between AI assistants and real‑world network data by exposing a lightweight, serverless interface to the ipinfo.io API. Developers can query an IP address and receive structured information—such as country, city, postal code, latitude/longitude, carrier, and organization—without writing any networking or API‑handling code. This is particularly useful for building conversational agents that need to adapt responses based on user location, enforce geo‑restrictions, or provide contextual insights during troubleshooting sessions.

By packaging the ipinfo.io service behind the Model Context Protocol, the server removes the need for AI clients to manage authentication or handle HTTP request/response cycles. Instead, a single tool call () is available to the assistant, and the server takes care of token management, rate limiting, and response validation. This abstraction lets developers focus on higher‑level logic—such as personalizing content or logging access patterns—while the MCP server guarantees consistent, typed data output.

Key features include:

• Simple tool invocation: One call () with a single argument returns a fully validated Pydantic model.
• Secure token handling: The server reads the from the environment, keeping secrets out of client code.
• Zero‑configuration resources: No additional resources or prompts are required; the server is plug‑and‑play.
• Fast, stateless operation: Each request is independent, making the server ideal for scaling behind cloud functions or edge runtimes.

Typical use cases span a broad spectrum:

• Geofencing – an assistant can refuse or modify service offers based on the user’s country.
• Localized support – automatically suggest regional help articles or contact numbers.
• Security monitoring – flag anomalous login attempts by correlating IPs with known threat actors.
• Analytics – aggregate visitor locations to inform marketing strategies or compliance reporting.

Integrating this MCP server into an AI workflow is straightforward: the assistant simply calls with the target IP, receives a structured response, and can then condition its subsequent actions. Because the server adheres to MCP standards, it works seamlessly with any client that understands the protocol—whether built on Claude, GPT‑4o, or a custom in‑house model. The result is a reliable, developer‑friendly tool that adds tangible geographic intelligence to conversational AI systems.