SuperMap MCP

Overview

The SuperMapMCP server bridges the gap between spatial data formats and modern AI assistants by providing a lightweight, MCP‑compliant interface for importing Shapefiles into a database. It solves the common pain point of converting geographic vector files into a structured, queryable form that AI models can interrogate through standard MCP tools. By exposing this capability over the Model Context Protocol, developers can seamlessly embed spatial analytics into conversational agents without handling low‑level file parsing or database migrations themselves.

At its core, the server accepts a Shapefile package via an MCP tool request and orchestrates the entire ingestion pipeline: it extracts geometry, attributes, and coordinate reference information; validates data integrity against the target database schema; and writes the records into a relational or spatial database using SuperMap’s Java object APIs. This automation removes manual steps such as creating data sources, defining feature classes, or writing custom ETL scripts. The result is a ready‑to‑query dataset that can be referenced in prompts, queried through the or capabilities of MCP clients, and combined with other AI‑generated insights.

Key features include:

• MCP Tool Integration – Exposes a single tool that accepts file uploads and database connection details, returning status and error messages in the MCP response format.
• SuperMap Compatibility – Leverages SuperMap’s robust Java libraries to handle complex geometry types, CRS transformations, and large file sets.
• Environment‑Aware Configuration – Uses a file for OpenAI credentials and database parameters, allowing secure, version‑controlled deployment.
• Cross‑Platform Runtime – Supports both Linux and Windows via a virtual environment wrapper, simplifying local development and CI pipelines.

Typical use cases are abundant in GIS‑centric AI applications. A city planning assistant could ingest updated land parcel Shapefiles, enabling the model to answer questions about zoning or infrastructure. In environmental monitoring, a chatbot could import species distribution maps and then generate risk assessments based on user queries. Any scenario that requires rapid, repeatable loading of spatial data into a database for downstream AI analysis can benefit from SuperMapMCP.

Integration is straightforward: once the server is running, an MCP‑enabled client (such as Claude) can invoke the import tool, receive confirmation, and then use the newly populated database as a data source for prompts or sampling. The server’s design prioritizes reliability and transparency, providing clear error reporting and logging so developers can troubleshoot ingestion failures quickly. This combination of ease of use, strong spatial data handling, and MCP compatibility makes SuperMapMCP a standout solution for developers looking to embed geographic intelligence into AI workflows.