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Satellite land due-diligence: a green/caution/walk-away verdict with cited signals for any parcel.
About
Satellite land due-diligence: a green/caution/walk-away verdict with cited signals for any parcel.
Security Report
This is a well-engineered MCP server with excellent security practices around API key handling. The code demonstrates thoughtful defensive design, particularly in preventing accidental credential exfiltration through requiring explicit base URL configuration and rejecting plaintext transmission. Input validation is solid, dependencies are minimal and legitimate, and the architecture is appropriate for its purpose. One minor finding regarding overly permissive type validation does not significantly impact the security posture. Supply chain analysis found 1 known vulnerability in dependencies (0 critical, 1 high severity). Package verification found 1 issue.
5 files analyzed · 3 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: TERRAIN_API_KEY
Environment variable: TERRAIN_BASE_URL
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-winds3753-landbenchmark-mcp": {
"env": {
"TERRAIN_API_KEY": "your-terrain-api-key-here",
"TERRAIN_BASE_URL": "your-terrain-base-url-here"
},
"args": [
"-y",
"landbenchmark-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub 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:
{
"mcpServers": {
"terrain": {
"command": "npx",
"args": ["-y", "landbenchmark-mcp"],
"env": {
"TERRAIN_API_KEY": "tk_live_...",
"TERRAIN_BASE_URL": "https://www.landbenchmark.com"
}
}
}
}
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
| Param | Type | Notes |
|---|---|---|
lat, lon | number | Parcel centre (WGS84). Provide these or geometry. |
geometry | GeoJSON | Polygon or Point (alternative to lat/lon). |
label | string | Optional parcel name. |
mode | "lite" | "full" | lite (default) ≈ 1 min; full = deep multi-year satellite report ≈ 3–4 min. |
Returns a plain-text verdict summary with the flagged signals and a link to the full report.
Build
npm install
npm run build # → dist/index.js
npm test # verifies the API-key safety guard
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.
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