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landuse-mcp

by contextualizer-ai
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Python
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landuse-mcp

A Model Context Protocol (MCP) server for retrieving land use data for given geographical locations using the National Land Cover Database (NLCD) and other geospatial datasets.

Features

  • Land Cover Data: Retrieve detailed land cover classifications for any coordinate

  • Soil Type Information: Get FAO soil type classifications for geographical points

  • Temporal Data: Access available dates for land use data at specific locations

  • MCP Integration: Full Model Context Protocol support for AI agents

  • Geospatial Processing: Built on robust geospatial libraries for accurate data retrieval

Related MCP server: Jupyter Earth MCP Server

Quick Start

# Install dependencies and set up development environment
make dev

# Run the MCP server
make server

# Run tests
make test-coverage

# Demo functionality
make demo

Installation

From PyPI (Recommended)

# Install with uvx
uvx landuse-mcp

From Source

# Clone the repository
git clone https://github.com/justaddcoffee/landuse-mcp.git
cd landuse-mcp

# Install in development mode
make dev

Usage

Command Line Interface

# Run the MCP server
landuse-mcp

# Or using uv
uv run landuse-mcp

Python API

from landuse_mcp.main import get_land_cover, get_soil_type, get_landuse_dates

# Get land cover data for Death Valley
land_cover = get_land_cover(36.5322649, -116.9325408, "2001-01-01", "2002-01-01")
print(land_cover)

# Get soil type for a location
soil_type = get_soil_type(32.95047, -87.393259)
print(soil_type)  # e.g., "Cambisols"

# Get available dates for land use data
dates = get_landuse_dates(36.5322649, -116.9325408)
print(dates)  # List of available dates

Testing MCP Protocol

# Test MCP handshake
make test-mcp

# Extended MCP testing
make test-mcp-extended

Integration with AI Tools

Claude Desktop

Add this to your Claude Desktop configuration file (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "landuse-mcp": {
      "command": "uvx",
      "args": ["landuse-mcp"],
      "cwd": "/path/to/landuse-mcp"
    }
  }
}

Claude Code

claude mcp add -s project landuse-mcp uvx landuse-mcp

Goose

goose session --with-extension "uvx landuse-mcp"

Available Tools

The MCP server provides three main tools:

  1. get_land_cover: Retrieve land cover data for coordinates with temporal range

  2. get_soil_type: Get FAO soil classification for a location

  3. get_landuse_dates: List available dates for land use data at coordinates

Data Sources

  • National Land Cover Database (NLCD): Primary source for US land cover data

  • FAO Soil Types: Global soil classification system

  • nmdc-geoloc-tools: Underlying geospatial processing library

Development

Development Setup

# Full development setup
make dev

# Install production dependencies only
make install

# Run tests with coverage
make test-coverage

# Code quality checks
make format lint mypy

# Check for unused dependencies
make deptry

# Clean build artifacts
make clean

Build and Release

# Build package
make build

# Upload to TestPyPI
make upload-test

# Upload to PyPI
make upload

# Complete release workflow
make release

Testing

# Run all tests
make test-coverage

# Run integration tests
make test-integration

# Test MCP protocol
make test-mcp test-mcp-extended

Contributing

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Run tests: make test-coverage

  5. Run quality checks: make format lint mypy

  6. Submit a pull request

Requirements

  • Python 3.10+

  • uv (recommended)

  • Dependencies managed via pyproject.toml

License

MIT License - see LICENSE file for details.

Authors

  • Mark Miller

  • Justin Reese

  • Charles Parker

Citation

If you use this software in your research, please cite it as:

@software{landuse_mcp,
  title = {landuse-mcp: A Model Context Protocol server for land use data},
  author = {Miller, Mark and Reese, Justin and Parker, Charles},
  url = {https://github.com/justaddcoffee/landuse-mcp},
  version = {0.1.0},
  year = {2024}
}

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Maintenance

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