
Landbenchmark Mcp
io.github.winds3753Published by GitHub account winds3753v0.1.1Updated Oct 24, 2026
Satellite land due-diligence: a green/caution/walk-away verdict with cited signals for any parcel.
Overview
Lets an assistant run satellite land due-diligence on a parcel and return a green, caution, or walk-away verdict with cited signals.
- What you can do
- Exposes one tool, analyze_parcel, which takes a parcel centre as latitude and longitude or a GeoJSON geometry, plus an optional label and a lite or full mode. It returns a plain-text verdict summary listing flagged signals such as flooding, slope and buildability, soil, hazards, and access, along with a link to the full report. Lite mode takes about a minute; full mode produces a deeper multi-year satellite report in roughly three to four minutes.
- When to use it
- Useful when someone asks an assistant whether a piece of land is worth buying and wants an answer grounded in observed satellite data rather than guesswork. Suited to early due-diligence screening of parcels before commissioning surveys or site visits.
- Requirements
- Runs locally over stdio, typically via npx from the npm package landbenchmark-mcp, so Node.js is needed. Requires a LandBenchmark API key in TERRAIN_API_KEY and a base URL in TERRAIN_BASE_URL; both are required and there is no default base URL. Network access to the configured host is needed, and the client must be desktop-based.
Installation
In SourceWeft
- Open Landbenchmark Mcp in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
landbenchmark-mcp
An MCP server that lets AI agents run satellite land due-diligence through LandBenchmark. It exposes one tool, analyze_parcel, that returns a green / caution / walk-away verdict with cited signals (flooding, slope & buildability, soil, hazards, access) for any parcel.
Why
When someone asks their AI assistant "is this land any good to buy?", the agent can call LandBenchmark and answer with observed, cited satellite data instead of guessing.
Setup
- Get an API key at landbenchmark.com/account → API keys.
- Add the server to your MCP client config:
Both env vars are required. (TERRAIN_* is the internal engine name — LandBenchmark runs on the Terrain analysis engine.) TERRAIN_BASE_URL has no default on purpose: every request sends your API key to that host in an Authorization header, so the server refuses to start rather than guess where it goes. It also refuses to send a key over plain http:// to anything but localhost.
Works with any MCP-capable client — Claude Desktop, Claude Code, and agent frameworks.
Tool: analyze_parcel
Returns a plain-text verdict summary with the flagged signals and a link to the full report.
Build
Links
- Product: https://www.landbenchmark.com
- API & MCP docs: https://www.landbenchmark.com/developers
Informational only — not a survey, flood determination, or a substitute for on-site inspection and professional advice.
Source: README.md at commit e837c12
Tools
0Version history
1- v0.1.1LatestSep 16, 2026
Score
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- Auth declared10 pts
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- Tools listed15 pts
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- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts