CropProphEU

Created By

DasClowna month ago

Overview

🌾 CropProphEU — EU Crop Intelligence MCP Server

13 MCP Tools, 5 Crops, 26 EU Countries, 123 NUTS2 Regions — Yield forecasts, market values (€/ha), risk analysis, environmental risk scoring & portfolio optimization for European agriculture. Built for AI agents, by AI agents.

"How will wheat perform in Sachsen-Anhalt this year? What's my best €/ha allocation across 100 ha?"

pip install git+https://github.com/DasClown/CropProphEU.git

Features (13 MCP Tools)

#	Tool	What it does	V
1	`yield_and_value`	Yield + market value (€/ha) + plain-language summary (DE/EN)	V4.6
2	`europe_yield_forecast`	Pan-European RF forecast with Yield-at-Risk (P10/P50/P90) + NDVI correction	V4.3
3	`crop_forecast`	Current season: GDD, rain, soil moisture, drought index, frost warnings	V4.0
4	`compare_regions`	Batch-compare 20 regions × 5 crops with live market prices	V5.1
5	`portfolio_optimizer`	AI investment engine: budget → optimal allocation across regions × crops	V5.1e
6	`season_comparison`	Compare this season to historical years	V4.0
7	`region_health`	All crops for one region, single call	V4.5
8	`weather_outlook`	16-day weather forecast	V4.0
9	`climate_scenario`	What-if: +2°C, -20% rain, etc.	V4.4
10	`yield_forecast`	Analog-year yield matching (DE-focused)	V3.0
11	`list_regions`	123 NUTS2 regions across 26 countries	V4.2
12	`list_crops`	Crop parameters (GDD base, season, frost sensitivity)	V4.5
13	`environmental_risk`	NEW V5.4 — ERS (forest, erosion, storm, hail) + wild boar damage risk for DE	V5.4

Quick Start

1. Install

pip install git+https://github.com/DasClown/CropProphEU.git

2. Use as MCP Server

CLI (stdio):

crop-mcp

Python API:

from crop_mcp import predict_europe_yield

result = predict_europe_yield("DE11", "DE", crop="wheat", gdd=3050, precip_mm=650)
print(f"Yield: {result['predicted_yield_t_ha']} t/ha")
print(f"Revenue: ~{result['predicted_yield_t_ha'] * 239:.0f} €/ha")

3. Claude Desktop / Cursor

{
  "mcpServers": {
    "crop": {
      "command": "python3",
      "args": ["-m", "crop_mcp.server"]
    }
  }
}

4. HTTP Server (Remote / Smithery)

pip install crop-mcp[http]
crop-mcp --http --port 8080

Connect via SSE: http://your-server:8080/sse

5. Docker

docker build -t crop-mcp .
docker run -p 8080:8080 crop-mcp crop-mcp --http --port 8080

Verified Crops — Data Integrity ✅

Every prediction traces to a verified Eurostat crop code. No hallucinations, no silent wrong-crop training.

Crop	Eurostat Code	Samples	Countries	MAE (LOYO)	R²
🌾 Wheat	C1100	1,603	26 (🇪🇺+🇺🇦)	11.5%	0.87
🌽 Corn (Maize)	C1500	1,797	21 (🇪🇺+🇬🇧)	11.6%	0.72
🌿 Barley	C1300	1,885	26 (🇪🇺+🇬🇧)	11.2%	0.85
🌻 Rapeseed	I1110	1,825	25	10.8%	0.83
🌻 Sunflower	I1120	1,229	17	16.1%	0.74

⚠️ V5.1d Data Fix: Rapeseed + Sunflower were previously trained on RICE data (wrong Eurostat codes C2000/C2200). Now corrected to Industrial crop codes I1110/I1120. DE rapeseed prediction fell from 7.21t to 2.63t — real, not extrapolated.

Why CropProphEU?

Capability	CropProphEU	Open-Meteo MCP	Gro Intelligence
Yield forecasts	✅ 5 crops	❌	✅ $10K+/yr
Soil features	✅ 11 properties	❌	✅
Yield-at-Risk (P10/P90)	✅	❌	✅
Live market prices (€/ha)	✅ CBOT + MATIF	❌	✅
Climate what-if	✅	❌	✅
Frost warnings	✅	✅	❌
NDVI satellite correction	✅	❌	❌
Portfolio optimizer	✅	❌	❌
Multi-language (DE/EN)	✅	❌	❌
Environmental Risk	✅ V5.4	❌	❌
Price	Free	Free	$10K+/yr

Unique: Only free MCP server covering EU agriculture with soil → yield → market value → environmental risk → portfolio optimization in one pipeline.

V5.4 — Environmental Risk Score + Wildschaden 🌍🐗

Feature	Beschreibung
Environmental Risk Score	Komposit aus Waldanteil, Maisfläche, Bodenerosion, Sturm- + Hagelrisiko → 🟢🟡🔴
Wildschaden DE	DJV-Jagdstreckendaten + Waldrandindex + Maisflächenanteil → €/ha-Verlustschätzung
Ampel-System	`🟢 low (<35)`, `🟡 moderate (35-65)`, `🔴 high (≥65)`
MCP Tool	`environmental_risk(region='DE26')` → sofortige Analyse inkl. Wildschaden

Beispiel Maßbach (DE26 Unterfranken):

{
  "overall_risk": "🔴 high",
  "ers_level": "🟡 moderate",
  "wild_boar_risk": {"level": "🔴 high", "loss_eur_ha": 158},
  "management": ["Waldrandstreifen 3-6m", "Drückjagd Nov-Dez", "8-Tage-Anzeigefrist §36 BayJG"]
}

V5.4 — Testing & CI 🧪

Maßnahme	Status
pytest	16 Tests, alle passing (`tests/test_crop_mcp.py`)
CI (GitHub Actions)	✅ Aktiv bei jedem Push — Badge im Header grün
Git LFS	`*.pkl` + große `.json` via LFS (aus Git-Tree entfernt)

Run tests:

pip install -e ".[test]"
pytest tests/ -v

Model Accuracy

Metric	Value
LOYO MAE (Wheat)	0.599 t/ha (11.5%)
Forward Validation (Train ≤2022, Test 2023-24)	0.794 t/ha (15.0%)
R² (LOYO)	0.871
R² (Forward)	0.628

Most accurate for core EU (DE, FR, BE, NL, AT, CZ) where training data is dense.

Per-Crop Performance (V5.2)

Crop	Algorithm	Top Feature	Key Insight
🌾 Wheat	RF 200 trees	solar_kwh (35%)	Nord/Süd gradient dominates
🌽 Corn	RF 200 trees	clay_pct (42%)	Maize is extremely soil-sensitive
🌿 Barley	Ridge	clay_pct (27%)	Best coverage of all crops
🌻 Rapeseed	RF 200 trees	coarse_pct (28%)	Corrected — now 1,825 real samples
🌻 Sunflower	Ridge	silt_pct (24%)	17 countries (post-fix)

Live Market Prices

Crop	Source	€/t (Mai 2026)	Market
Wheat	✅ CBOT ZW=F + MATIF premium	239	Euronext MATIF
Corn	✅ CBOT ZC=F + MATIF premium	189	Euronext MATIF
Barley	✅ Reference (AMI regional)	190	AMI regional exchanges
Rapeseed	✅ Reference	470	Euronext MATIF (ECO)
Sunflower	✅ Reference	420	ICE / Black Sea

Production costs are country- and crop-specific (V5.3+). See market_prices.py → COUNTRY_PRODUCTION_COSTS (28 EU countries × 5 crops). Sources: FADN, KTBL, ARVALIS. Examples:

Country	Wheat	Corn	Barley	Rapeseed	Sunflower
🇩🇪 DE	650	700	600	780	650
🇫🇷 FR	700	650	600	750	600
🇵🇱 PL	550	600	500	650	550
🇷🇴 RO	450	500	400	550	450
🇪🇸 ES	600	650	550	700	—

ℹ️ Full table: COUNTRY_PRODUCTION_COSTS dict in crop_mcp/market_prices.py

Data Sources

Source	Data	Access
Eurostat	Crop yields (`apro_cpshr`) — 25+ years, verified codes	Free, no key
NASA POWER	GDD, precip, solar, soil moisture	Free, no rate limits
Open-Meteo	16-day forecast, GDD	Free, no key
SoilGrids v2 (ISRIC)	11 properties: SOC, pH, N, CEC, clay, sand, silt, bdod (bulk density), cfvo (coarse fragments), AWC, coarse	Free REST API
LUCAS Soil (ESDAC)	Texture ~20K field points + coarse fragments	Free download
Sentinel-2 NDVI	Vegetation index (Copernicus STAC + Planetary Computer fallback)	Free, no auth
Yahoo Finance	Live CBOT wheat/corn futures, EUR/USD	Free, no key

Zero API keys required — all sources are free and public.

Example Output

German (default):

Weizen – Region DEE0 (DE)
Ertrag: 7.35 t/ha (Spanne 6.50–8.20)
Temperatur: warm (2950°C Wärmesumme)
Niederschlag: ausreichend (480 mm)
Bodenfeuchte: feucht (48%)
Modellabweichung: ±11.5% (1603 Samples, 26 Länder)
Vergleich zu 2024: +0.15 t/ha (im Rahmen des Vorjahres)
Marktwert: 1.757 €/ha @ 239 €/t
Kosten: 650 €/ha → Deckungsbeitrag: 1.107 €/ha

English (with language="en"):

Wheat – Region DEE0 (DE)
Yield: 7.35 t/ha (range 6.50–8.20)
Temperature: warm (2950°C GDD)
...

Architecture

crop-mcp/
├── crop_mcp/
|├── server.py                 # 857 Zeilen — Pydantic-Modelle + Tool-Registry + MCP-Init
│   ├── tools/                  # **V5.4e** — Handler in logische Module aufgeteilt
│   │   ├── weather.py          # weather_outlook, crop_forecast, season_comparison, region_health
│   │   ├── yield_tools.py      # yield_forecast, europe_yield_forecast, yield_and_value, climate_scenario
│   │   ├── market.py           # compare_regions, portfolio_optimizer
│   │   ├── info.py             # list_regions, list_crops
│   │   ├── environmental.py    # environmental_risk (V5.4)
│   │   └── helpers.py          # Shared utilities (NDVI correction, frost, language)
│   ├── europe_model_api.py       # RF (200 trees) + Yield-at-Risk + NDVI correction
│   ├── environmental_risk.py     # V5.4 — ERS + Wildschaden DE
│   ├── ndvi_correction.py        # Sentinel-2 NDVI correction factor (±30%)
│   ├── market_prices.py          # Live CBOT/MATIF via Yahoo Finance
│   ├── feature_cache.py          # Sub-second historical queries
│   ├── simulate_yield.py         # Analog-year matching
│   ├── auto_update.py            # Monthly retrain cron
│   ├── core/regions.py           # 123 NUTS2 regions
│   └── sources/                  # Weather, soil, NDVI, Eurostat, FAOSTAT fetchers
├── models/                       # .pkl files ➜ download from Releases
├── data/                         # Training data ➜ download from Releases (or generated by build)
├── tests/                        # V5.4 — 16 pytest tests
├── .github/workflows/ci.yml      # V5.4 — CI workflow (lokal, benötigt PAT mit workflow-Scope für Push)
├── pyproject.toml
└── README.md

Key design principles:

No hallucination — every yield prediction traces to verified Eurostat data
Live prices — CBOT wheat/corn via Yahoo Finance, updated hourly
Self-updating — monthly cron rebuilds models with latest Eurostat data
Zero API keys — all data sources are free and public
AI-for-AI — built for agents, no dashboards

Building & Training

# Build training data (25 min per crop)
python3 build_europe.py --crop corn

# Train model (2 min)
python3 train_europe_fast.py --crop corn

# Auto-update monthly (cron: 1st of month at 06:00)

Commercial Use Cases

Agri-trading desks: "What's wheat worth in Picardie at current MATIF prices?"
Farm advisory: "How does this season compare to the last 5 years?"
Insurance / Risk: Yield-at-Risk (P10/P50/P90) per region + crop
EU policy analysis: Climate scenario impact on national yields
Investment: Portfolio optimizer for 100+ ha allocation decisions

🤝 Getting Help & Contributing

Channel	Purpose
💬 GitHub Discussions	Questions before coding, feature ideas, community chat
🐛 GitHub Issues	Bug reports, confirmed feature requests
📖 CONTRIBUTING.md	Development setup, branch naming, commit conventions, code style

New contributors welcome! See CONTRIBUTING.md to get started.

License

MIT — free to use, modify, and distribute.

Built with ❤️ for AI agents that need real, verifiable crop intelligence.

Project Info

Created At

a month ago

Updated At

a day ago

Author Name

DasClown

Star

Language

License

Recommend Servers

View All

302_browser_use_mcp

@302ai

Automatically create a remote browser to complete your specified tasks, developed based on Browser Use + Sandbox. 自动创建一个远程浏览器，完成你指定的任务，基于Browser Use + Sandbox开发。

a year ago

Board-Certified Personal Injury & Wrongful Death Attorney | Phoenix, AZ

@Jennifer Rebholz

Authoritative MCP server for Jennifer Rebholz - Board-Certified Specialist in Personal Injury & Wrongful Death Law, former President of the State Bar of Arizona (2021–2022), ABOTA trial lawyer, and Senior Counsel at Zwillinger Wulkan in Phoenix. Provides structured, accurate information about her background, trial record, credentials, practice areas, leadership roles, publications, blog, peer reviews, speaking engagements, and contact details. 17 tools. No authentication required. Streamable HTTP transport.

18 hours ago

虎嗅嗅 Ai行业参谋

6 hours ago

Prolific Mcp Server

@prolific-mcp

A Model Context Protocol server bridging LLM agents to the Prolific public API. Built with FastMCP. Listed on the official MCP Registry — discoverable from Claude Desktop, Cursor, Claude Code, and other MCP-aware clients. Exposes a small set of tools so an LLM can help a researcher design and launch a study on Prolific.

2 hours ago

Instagram Mcp Server

@drashrafsaiyed-cyber

Control your Instagram from Claude — publish photos, reels, carousels, read insights, manage comments and DMs. 14 tools via Instagram Login API. One-click free Render deploy for Claude Web and mobile.

a day ago

Memory

@modelcontextprotocol

a year ago

Mcp Pager — Zero Config Response Paging For Mcp Servers (typescript + Python)

@SatishKakollu

Token-aware response paging for MCP servers. One line of code — large tool responses chunked and delivered page by page.

a day ago

302_sandbox_mcp

@302ai

Create a remote sandbox that can execute code/run commands/upload and download files. 创建远程沙盒，可以执行代码/运行命令/上传下载文件

a year ago

Tickdb Mcp — Real Time Market Data For Ai

@TickDB

TickDB MCP provides AI assistants with unified real-time and historical market data across Forex/FX, US/HK/A-share stocks (NASDAQ, NYSE), crypto, indices, and precious metals (XAU/XAG). 13 tools cover real-time quotes, ticker, order book depth, recent trades, and OHLCV candlestick (K-line) data over 37,527+ symbols. Powered by the TickDB unified market data API.

6 hours ago

Google Maps

@modelcontextprotocol

Location services, directions, and place details

a year ago

Conversor IAE-CNAE — MCP for Spanish tax data

@Conversor IAE CNAE

The first fiscal MCP server for Spain. Autónomos and gestorías lose hours mapping IAE ↔ CNAE 2025 codes and guessing which AEAT forms (036/130/303/347…) apply — and general AI agents confidently hallucinate tax facts. This server gives any MCP client a direct line to the real data: 1,187 IAE codes (AEAT), 1,060 CNAE 2025 codes (INE), the IAE↔CNAE crosswalk, IRPF/IVA módulos, AEAT obligations and the 2026 RETA brackets — answering from official sources instead of guessing. 6 of the 10 tools are free (no key, no signup); 4 premium tools (obligations, módulos, bulk lookup, RETA quota) target gestorías and fintech, from €29/mo. Spain-only, by design. Every tool carries the `readOnlyHint` annotation.

39 minutes ago

MiniMax MCP

@MiniMax-AI

Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.

Python

a year ago

Filesystem

Secure file operations with configurable access controls

a year ago

a day ago

Budget & cost control for AI agents: hard per-agent spend caps, rate limits, idempotency, and human-in-the-loop approval — enforced before each LLM call, not after the invoice. One hosted MCP endpoint (no proxy or self-hosting), settled via x402 (USDC on Base).

2 days ago

Voyei

33 minutes ago

Cricketstudio Mcp

@i-m-arul

29 MCP tools for IPL 2026, IPL historical (18 seasons), and Major League Cricket. Free, no API key.

a day ago

LADU Adu Tools

@sirbots

LADU’s MCP server connects AI assistants directly to LA County ADU data. Ask about any address and get a plain-English buildability verdict, the parcel’s zoning and regulatory overlays (hillside, coastal, fire, flood, historic, fault zones), and the governing city’s ADU rules. Query permit statistics for any of 51 supported LA cities — totals, year-over-year trends, and valuation distributions from 19,000+ cleaned permit records going back to 2018. Compare two cities side by side, estimate project costs by type and finish level, or find permitted ADUs near an address.

21 hours ago

Playwright Mcp

@microsoft

Playwright MCP server

TypeScript

10 months ago

Email Outreach by OpenHelm

@Max Beech

Run personalised outreach campaigns and manage conversations using a virtual computer equipped with email capabilities, research tools, and workflow automation. Email Outreach by OpenHelm enables your agent to carry out work such as: • Prospect research • Personalised outbound email campaigns • Lead qualification • Follow-up sequences • Inbox monitoring and response handling What makes OpenHelm the best solution for this: • AI continuously monitors conversations and adjusts actions to maximise outcomes • Deep research capabilities enable highly personalised outreach at scale • Multi-step workflows coordinate research, sending, follow-ups, and response management • Operates within a secure remote environment isolated from your local machine For organisations looking to generate opportunities through outbound communication, OpenHelm provides the infrastructure needed to run sophisticated outreach workflows autonomously.

42 minutes ago

SEO & GEO by OpenHelm

@Max Beech

Improve your visibility across both traditional search engines and AI-powered answer engines using a virtual computer that can analyse websites, competitors, content, and search performance. SEO & GEO by OpenHelm enables your agent to carry out work such as: • SEO audits and recommendations • GEO (Generative Engine Optimisation) analysis • Competitor monitoring • Content opportunity discovery • Search visibility reporting What makes OpenHelm the best solution for this: • Continuously evaluates whether recommendations are actually improving visibility over time • Can investigate competitors and search results using a highly capable stealth browser • Multi-step workflows allow analysis, planning, implementation, and monitoring to work together • Designed for both search engines and emerging AI discovery platforms For organisations looking to grow organic visibility, OpenHelm provides a secure environment where optimisation work can be performed continuously and autonomously.

42 minutes ago

Mcp360 Universal Gateway

@mcp360

A universal gateway MCP server providing seamless integration with multiple AI models and services through a unified interface.

5 hours ago

Docgraph

@Detective-XH

A Go MCP server that indexes .md/.docx/.html/.pdf into a searchable knowledge graph and runs drift audits to flag stale, conflicting, or undocumented content — letting agents govern documents like code.

2 days ago

Agent Tools Mcp

@AgentTools-Cloud

Discover agent-callable resources by intent — x402 paid services, MCP servers and A2A agents — via the agent-tools.cloud directory. 9 tools: search/get/list_categories (x402), search_mcp_servers/get_mcp_server, search_agents/get_agent, search_all, stats.

8 hours ago

Airtreks Mcp

@SEKeener

a day ago

Fabric

@fabric-agent-plugins

4 hours ago

Fetch

@test

Web content fetching and conversion for efficient LLM usage

8 months ago

Jina AI MCP Tools