The Automated Smart Irrigation And Soil Health Pipeline — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited The Automated Smart Irrigation And Soil Health Pipeline (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
An MCP-Driven Agronomic Intelligence System with Built-In Hardware Safety Guardrails An intelligent, closed-loop environmental agent architecture designed to optimize agricultural water consumption and audit crop soil conditions. Built for the Agents for Good track of the Kaggle AI Agents: Intensive Vibe Coding Capstone Project.
Traditional agricultural automation systems rely on rigid, static threshold rules (e.g., if moisture < 30%, turn on water). These configurations break down when confronting complex real-world variables, such as ignoring predictive weather APIs or blindly trusting inaccurate rain forecasts while crops actively suffer from dehydration stress.
This project implements an adaptive multi-agent reasoning framework powered by Gemini 2.5 Flash and structured using the Model Context Protocol (MCP). The pipeline evaluates live sensor data, detects logical conflicts between atmospheric forecasts and ground truth metrics, automatically accounts for rain-induced nutrient leaching, and actuates irrigation hardware safely through integrated operational guardrails.
This project demonstrates three core course concepts to satisfy the capstone technical evaluation matrix:
google-genai SDK, our agent processes complex telemetry and makes contextual agronomic decisions (balancing temperature, moisture, and weather trends) instead of relying on brittle, hardcoded if/else logic loops.[ MCP Server Resource ] ----> [ Validated Data Schema ] ----> [ Hardware Guardrail ] (Simulated Telemetry) (Moisture, Temp, Forecast, pH) (Max 50% Moisture Check) [ Executable MCP Tool ] <==== [ AI Agent Decision Loop ] <========
A standout feature of this agent is its ability to handle multi-variable environmental relationships:
smart-irrigation-pipeline/
├── src/
│ ├── __init__.py
│ ├── schema.py # Pydantic data schemas verifying telemetry integrity
│ ├── mcp_server.py # FastAPI application exposing MCP resources and tools
│ └── agent.py # Core ADK agent containing logic and guardrails
├── requirements.txt # Project dependencies
└── README.md # Project report and setup instructions
⚡ Getting Started & Setup Instructions
1. Install Dependencies
Ensure you are running within an isolated Python virtual environment, then install the required packages:
pip install -r requirements.txt
2. Configure Your Environment Workspace
Set up your Gemini authorization key within your local environment terminal:
export GEMINI_API_KEY="your_actual_gemini_api_key_here"
3. Launch the MCP Server
Start up the FastAPI server to begin exposing live telemetry resource feeds on port 8000:
python -m uvicorn src.mcp_server:app --reload --port 8000
4. Execute the Pipeline Agent
In a separate terminal window, execute the smart agent to run a diagnostics loop and actuate hardware tasks:
python src/agent.py
=====+ (Passed if safe)
(Actuate Irrigation) (Gemini 2.5 Flash Reasoning)~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.