GitHub - VasileiosTs/agribrain: Agronomic intelligence for AI agents / weather, ET₀, GDD, spray windows, soil. Zero API keys. MCP server + core library. · GitHub
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agribrain
Give your AI assistant a licensed agronomist's brain.
[DEMO GIF GOES HERE — 15s: real field coordinates → "What's the spray situation<br>and water balance for my olives this week?" → real answer with numbers]
Every LLM can write a poem about olive trees. None of them know that your<br>olive fruit fly third generation started Tuesday, that Saturday's wind makes<br>spraying pointless, or that your field is 12mm behind on water. This MCP<br>server fixes that — using only free, open data. No API keys. No accounts.
npx agribrain
Claude Desktop setup (60 seconds)
"mcpServers": {<br>"agri": { "command": "npx", "args": ["agribrain"] }
Then ask: "Check the field briefing for 38.01, 23.72 — olives, planted March 2024."
Tools
Tool<br>What it answers
get_field_briefing<br>"What should I worry about this week?" — the everything report
get_spray_windows<br>"When can I actually spray?" — ranked windows with reasons
get_water_balance<br>"Am I irrigating enough?" — ET₀ loss vs. water received, net deficit
compute_gdd<br>"Where is the pest pressure?" — degree-days, generations, projected dates
get_chill_hours<br>"Did my orchard get enough winter chill?"
get_agro_weather<br>Forecast + recent history, in farming terms
get_soil_profile<br>pH, texture, organic carbon for any point on Earth
What makes this different
This is not another weather wrapper. Every model in this repo — pest<br>degree-day thresholds, spray-window rules, crop staging — is curated and<br>signed off by a licensed agronomist with 19 years in Mediterranean<br>agriculture, with literature citations in the data files. Data is cheap;<br>agronomy is the hard part.
Decisions, not just data — spray windows, water deficits, generation timing
Zero keys, zero cost — Met.no, NASA POWER, ISRIC SoilGrids (free, commercial-friendly)
Citations included — every pest model links its sources
Eval suite in the repo — we test that LLMs actually answer correctly with these tools
Data sources & attribution
Weather forecasts: MET Norway (CC-BY 4.0).<br>Historical climate: NASA POWER (public domain).<br>Soil: ISRIC SoilGrids (CC-BY 4.0).<br>ET₀: computed via Hargreaves; FAO-56 (Allen et al., 1998).
Honest limitations
GDD outputs without local trap data are estimates and labeled as such.
SoilGrids is 250m resolution — a default, not a substitute for a soil test.
This tool informs decisions; it does not replace your local agronomist or<br>the product label. Nothing here is application-rate advice.
Roadmap
v2 — eyes on the field: Sentinel-2 NDVI time series and zone anomaly<br>detection for any field polygon (free Copernicus data), FAO-56 crop water<br>demand (Kc × ET₀), 30-year climate context.
v3 — compliance: EU pesticide approval checks, pre-harvest intervals,<br>resistance groups.
Who builds this
Built and maintained as the open data layer of Ask Oli<br>— the AI agronomist for Mediterranean smallholders. Maintained part-time by a<br>solo founder; issues are triaged weekly, agronomy contributions (pest models<br>for your region — see data/pest-models.json schema) are especially welcome<br>and reviewed personally.
MIT licensed.
About
Agronomic intelligence for AI agents / weather, ET₀, GDD, spray windows, soil. Zero API keys. MCP server + core library.
Topics
agriculture
mcp
open-data
farming
gdd
agronomy
ai-agents
et0
spray-windows
Resources
Readme
License
MIT license
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