MCP Server Proj

Overview

The MCP Server PROJ is a dedicated Model Context Protocol server that brings coordinate system transformations and map projections into AI‑driven workflows. By exposing a small, well‑defined set of tools over the MCP protocol, it allows language models such as Claude to request geographic conversions on demand, turning raw latitude/longitude pairs into any projected coordinate system required by downstream applications. This solves a common pain point for developers who need reliable, standards‑compliant transformations without embedding heavy GIS libraries directly into their AI agents.

At its core, the server offers two principal tools: and . The former accepts a source CRS, target CRS, and an array of points, returning the transformed coordinates in a single round‑trip. The latter provides an inventory of all CRS identifiers supported by the underlying library, enabling dynamic discovery and validation. The server can run as a standalone service or be imported directly as a library, giving teams the flexibility to choose the deployment model that best fits their infrastructure. When running as a library, developers can instantiate a , configure source and target systems, initialize the transformer once, and then batch‑process points efficiently.

Key capabilities include:

• Multi‑format CRS support: EPSG codes, WKT definitions, and Proj string formats are all accepted, ensuring compatibility with a wide range of data sources.
• Batch transformation: Process thousands of points in a single request, dramatically reducing latency compared to per‑point calls.
• Intuitive API: The tool definitions are simple JSON structures, making integration straightforward for any MCP‑compliant client.
• Dual‑mode operation: Choose between a lightweight server for distributed environments or an in‑process library for tight integration.

Typical use cases span from geospatial data pipelines that need to reproject sensor outputs for mapping services, to real‑time navigation assistants that translate GPS coordinates into map tiles, and even environmental modeling tools that require consistent projection across multiple datasets. By delegating the heavy lifting of coordinate conversion to a dedicated MCP server, AI assistants can focus on higher‑level reasoning while ensuring geographic precision and compliance with international standards.

What sets this server apart is its MCP‑centric design: it plugs directly into any AI workflow that already speaks the Model Context Protocol, eliminating the need for custom adapters or manual API wrapping. The combination of a minimal surface area, robust batch processing, and full CRS coverage makes MCP Server PROJ an essential component for developers looking to embed reliable geospatial transformations into conversational AI or automated data pipelines.