FastMCP server for Python-native GDAL ops (Rasterio/PyProj/pyogrio|Fiona/Shapely)
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"gdal-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# GDAL MCP **Geospatial AI with epistemic reasoning** GDAL MCP is a Model Context Protocol (MCP) server that provides AI agents with geospatial analysis capabilities while requiring them to **justify their methodological choices** through a reflection middleware system. **🎉 v1.1.1 Released (2025-10-26)** — Vector tool parity + cross-domain reflection validated **🧠 Reflection System** — Domain-based epistemic reasoning that transcends data types **⚡ 75% Cache Hit Rate** — Methodology reasoning carries across raster ↔ vector operations [](https://github.com/Wayfinder-Foundry/gdal-mcp/actions/workflows/ci.yml) [](LICENSE) [](https://www.python.org/downloads/) [](https://github.com/jlowin/fastmcp) [](https://pepy.tech/projects/gdal-mcp) --- ## 📚 Documentation - **[Quick Start](QUICKSTART.md)** - Installation, setup, and MCP configuration - **[Tools Reference](TOOLS.md)** - Complete tool documentation with examples - **[Environment Variables](docs/ENVIRONMENT_VARIABLES.md)** - Runtime configuration and tool surface controls - **[Vision](docs/VISION.md)** - Long-term roadmap and philosophy - **[Changelog](CHANGELOG.md)** - Release history and updates --- ## 🧠 The Reflection System ### What Makes GDAL MCP Different? Most AI tool systems execute operations immediately when requested. GDAL MCP requires the AI to **justify methodological decisions** before execution, creating a conversation about the "why" rather than just executing the "what." **Traditional AI tool approach:** ``` User: "Reproject this DEM to …
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.