No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by JordanGunn · MCP Server · ★ 73
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gdal-mcp safe to install? View the security audit →
gdal-mcp MCP server exposing GDAL/Rasterio operations to AI agents, with a reflection middleware that requires structured justification before executing operations whose methodology matters (CRS choice, resampling method, query extent). Install Via uvx (recommended) Via Docker Local development Configure your MCP client Claude Desktop Add to (macOS: , Windows: , Linux: ): json { "mcpServers": { "gdal-mcp": { "command": "
| Stars | 73 |
| Forks | 8 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77.5634965591893/100 |
| Last Updated | 2026-05-25 |
| Created | 2025-09-05 |
| Platforms | mcp, python |
| Est. Tokens | ~67k |
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gdal-mcp is Model Context Protocol server that packages GDAL-style geospatial workflows through Python-native libraries (Rasterio, GeoPandas, PyProj, etc.) to give AI agents catalog discovery, metadata intelligen. It is categorized as a MCP Server with 73 GitHub stars.
gdal-mcp is primarily written in Python. It covers topics such as earth-observation, gdal, geospatial.
You can find installation instructions and usage details in the gdal-mcp GitHub repository at github.com/JordanGunn/gdal-mcp. The project has 73 stars and 8 forks, indicating an active community.
gdal-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to gdal-mcp on Agent Skills Hub include mazzap, cesium-mcp, emem. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: