agriculture-mcp-server
# Agriculture MCP Server
MCP server providing AI agents with agriculture and farming data — soil conditions, crop weather, climate history, global statistics, and food products.
[](https://glama.ai/mcp/servers/AiAgentKarl/agriculture-mcp-server)
## 8 Tools in 4 Categories
### Soil Conditions
- `soil_conditions` — Soil temperature (0-54cm), moisture, evapotranspiration forecast
### Crop Weather
- `crop_weather_forecast` — Agricultural weather: temp, rain, wind, radiation, water balance
- `climate_history` — Historical daily climate data since 1981 (NASA POWER)
- `climate_averages` — Long-term monthly climate averages for site assessment
### Global Statistics (World Bank)
- `country_agriculture_profile` — Full agriculture profile of any country
- `compare_countries` — Compare agriculture indicators across countries
### Food Products (Open Food Facts)
- `food_product_lookup` — Look up food products by barcode (nutrition, eco-scores)
- `food_search` — Search 3M+ food products by name or category
## Installation
```bash
pip install agriculture-mcp-server
```
## Usage with Claude Code
`.mcp.json`:
```json
{
"mcpServers": {
"agriculture": {
"type": "stdio",
"command": "python",
"args": ["-m", "src.server"]
}
}
}
```
## Data Sources
All APIs are **free and require no API key**:
| API | Data |
|-----|------|
| Open-Meteo | Soil temperature, moisture, evapotranspiration, crop weather |
| NASA POWER | Historical climate data since 1981 (agricultural community) |
| World Bank | Country-level agriculture statistics (20+ indicators) |
| Open Food Facts | 3M+ food products with nutrition and eco-scores |
## License
MITTDQS
Scored across 8 tools
Tools are generally well-differentiated by domain (climate vs. soil vs. country stats vs. food). The pair `climate_averages` and `climate_history` could cause slight confusion as both retrieve historical NASA POWER climate data for a location, though they are distinguished by monthly averages versus specific date ranges. Food tools are clearly separated by barcode lookup versus text search.
Most tools follow a consistent noun_phrase pattern (e.g., `climate_averages`, `soil_conditions`, `country_agriculture_profile`). However, `compare_countries` uses a verb_noun structure, creating a minor deviation from the otherwise consistent convention. All use snake_case consistently.
Eight tools is an appropriate count for this scope, covering field-level environmental data (climate averages, history, forecasts, soil), macro-level country agricultural statistics (profile and comparison), and food product databases (search and lookup) without redundancy or bloat.
The toolset provides comprehensive read-only coverage across distinct agricultural data domains: temporal climate data (past averages, historical range, forecasts), soil conditions, country-level indicators, and food product information. No obvious CRUD gaps exist for a data-retrieval server, though it lacks analytical tools that might combine these datasets (e.g., crop suitability scoring).