# landbenchmark-mcp

> landbenchmark-mcp — winds3753-landbenchmark-mcp. Use this tool when you need to assess the suitability of land parcels for development or investment, as it provides a comprehensive analysis of environmental and accessibility factors. The landbenchmark-mcp server takes lat/lon or GeoJSON geometry inputs and returns a verdict with cited signals, such as flooding, slope, and road access. It solves problems related to due diligence and risk assessment in land development, offering a data-driven approach to informed decision-making.

Canonical page: https://skillsregistry.net/skills/winds3753-landbenchmark-mcp  
JSON: https://api.skillsregistry.net/v1/skills/winds3753-landbenchmark-mcp

## Description

An MCP server that lets an AI agent run satellite land due-diligence through LandBenchmark. Its analyze_parcel tool takes a lat/lon or GeoJSON geometry and returns a green / caution / walk-away verdict with cited signals — flooding, slope, wildfire, road access, and more — for any parcel.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/gjl800o36y)
- **Repository:** <https://github.com/winds3753/landbenchmark-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "winds3753-landbenchmark-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/winds3753-landbenchmark-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/winds3753-landbenchmark-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt