Real-world examples demonstrating MSL (Macincode Scripting Language) capabilities across different industries using .mcn files
Hospital patient intake system that automatically analyzes vital signs, generates risk assessments, and alerts medical staff for critical conditions.
// Patient health assessment
var vitals = patient_data.vitals
var health_assessment = ai("Analyze vital signs: " +
"BP: " + vitals.blood_pressure +
", HR: " + vitals.heart_rate)
// Critical condition check
if vitals.blood_pressure > 140
trigger("https://hospital.com/api/alerts", {
"patient_id": patient_id,
"alert_type": "critical_vitals"
})Create an MCP server that exposes MSL functions as standardized tools for AI assistants.
// Register MSL tools for MCP
var msl_tools = [
{
"name": "query_database",
"description": "Execute SQL queries",
"handler": "msl_query_handler"
},
{
"name": "ai_analyze",
"description": "AI data analysis",
"handler": "msl_ai_handler"
}
]
// Handle MCP requests
function handle_mcp_request()
if method == "tools/call"
return execute_msl_tool(params.name, params.arguments)End-to-end ML pipeline for customer churn prediction, from data collection to model deployment.
// ML Pipeline automation
var training_data = query("SELECT * FROM customer_data")
var model_config = {
"name": "churn_predictor",
"features": ["age", "tenure", "charges"]
}
// Train model
var training_response = trigger("https://ml-platform.com/api/train",
model_config, "POST")
// Monitor and deploy
if model_metrics.accuracy > 0.85
trigger("https://ml-platform.com/api/deploy",
{"model_id": model_id})Executive dashboard that generates business insights, detects anomalies, and sends reports to stakeholders.
// Parallel data collection
task "revenue" "query" "SELECT SUM(amount) FROM orders"
task "customers" "query" "SELECT COUNT(*) FROM customers"
task "conversions" "query" "SELECT COUNT(*) FROM conversions"
var metrics = await "revenue" "customers" "conversions"
// AI insights
var insights = ai("Analyze business metrics: " +
"Revenue: " + metrics[0] +
", Customers: " + metrics[1])
// Anomaly detection
if revenue < avg_revenue * 0.7
trigger("https://slack.com/webhook", {
"text": "Revenue anomaly detected!"
})# Healthcare example
python run_msl.py run use-cases/healthcare_patient_management.mcn
# MCP Server (serve as API)
python run_msl.py serve --file use-cases/mcp_server_creation.mcn --port 3000
# ML Pipeline
python run_msl.py run use-cases/ml_model_training.mcn
# BI Dashboard
python run_msl.py run use-cases/bi_analytics_dashboard.mcn# Serve all use cases as REST endpoints
python run_msl.py serve --dir use-cases/ --port 8080
# Access endpoints:
# POST http://localhost:8080/healthcare_patient_management
# POST http://localhost:8080/mcp_server_creation
# POST http://localhost:8080/ml_model_training
# POST http://localhost:8080/bi_analytics_dashboardtask "data_fetch" "query" "SELECT * FROM users"
task "api_call" "trigger" "https://api.com/data"
var results = await "data_fetch" "api_call"var analysis = ai("Analyze patient vitals: " + vital_signs)
var insights = ai("Generate business insights from: " + metrics)trigger("https://slack.com/webhook", {"text": "Alert message"})
var response = trigger("https://api.com/data", payload, "POST")Download and run these real-world MSL (Macincode Scripting Language) .mcn examples in your environment
AI-powered patient monitoring with automated alerts
Create Model Context Protocol server for AI assistants
Automated machine learning model training and deployment
Real-time business intelligence with AI insights
git clone <repository> && cd msl
Click "Download .mcn" button above for any use case
Use the "Run Command" to execute the downloaded .mcn file