mcp · Global

mazzap

Mazzap, part of the Mazzstack: essentials for the Singularity Slowlife

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Data offered docs-verified: 3D terrain from any DEM · Aerial imagery draped on terrain · Classified LiDAR point cloud (ground, vegetation, ASPRS class, intensity) · Vegetation/tree derivation (LiDAR stems, canopy-height model, NDVI) · Terrain hydrology: flow paths, wetness index, ponding, seeps, storm/snowmelt runoff scenarios · Evapotranspiration and water balance (FAO-56 ET0, actual ET) · Wildfire behaviour scenarios (LANDFIRE fuels) · Solar siting (radiation, PV yield, shade) · Viewshed and distant terrain · Imported vector/raster geospatial layers (Atlas)
Coverage docs-verified: User-defined AOI (engine is region-agnostic); US-wide only for the bundled us-national vegetation pack
Formats docs-verified: Vector and raster geospatial files (Atlas import) · PLY-style point cloud (viewer)
Access docs-verified: Open-source local install (licence MIT per entry metadata, not stated in docs excerpt); requires Node >= 18 and Python 3 with GDAL; an LLM API key is needed only for the chat path

Use cases llm-inferred: Drainage screening: map flow paths, ponding depth and wetness index from a LiDAR DTM · Storm runoff screening using SCS Curve Number scenarios (local soils data would replace SSURGO) · Landscape/visual assessment: viewshed from a proposed observer point
Audience llm-inferred: drainage engineers · hydrologists · GIS analysts · landscape and planning consultants

Constraints llm-inferred: Docs describe US-based inputs (LANDFIRE, Daymet, SSURGO, NAIP, 3DEP); no UK data sources documented, and entry metadata's 'Global' region is unverified · Hydrology discharge carries a ±50% class band because the catchment is ungauged · Absolute annual AET is only ±20–35% without local flux-tower or gauge validation · Wildfire outputs are scenario-grade, not operational forecasts; spread is more reliable than times or intensities · Not a hosted data API: serves a locally built twin; a fresh clone has no place until npm run demo or npm run init is run (needs internet and GDAL) · Chat/MCP path needs the Python mcp SDK and an LLM key (OPENAI_API_KEY per entry auth metadata) · MCP tool names and transport are not documented in the excerpt · Maintenance status is not evidenced in the docs

Licence: MIT
Authentication: API key via env: OPENAI_API_KEY, OPENAI_REQUIRE_USER_KEY, VEIL_LIVE_TOKEN, VEIL_UID (auto-extracted from README)
GitHub stars: 152
Last commit: 24/07/2026
Last successful page check: 14/10/2026
Page health: healthy
Package: Not recorded
Verification: Not independently verified

Auto-extracted, unverified fields: tools, install, auth. Check upstream documentation.

Daily discovery 2026-10-15: GitHub search; verified via GitHub API. Tools/install/auth auto-extracted from README.

Metadata observed 14/10/2026 · daily-2026-10-15 (github-search)