MCP ML Monitor
Generates Ansible playbooks for automated remediation actions based on monitoring insights.
Integrates with GitHub for version control, triggering workflows, or managing model code changes.
Enables deployment actions and integration with Kubernetes for automated retraining or rollback.
Exports monitoring metrics to Prometheus for observability and alerting.
Sends alert notifications to Slack channels when drift or performance degradation is detected.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP ML MonitorCheck my production model for drift"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Agent for ML Model Monitoring & Drift Detection
Problem Statement
ML models degrade silently in production. Data distributions shift, feature relationships change, and model performance drops without immediate signals. This agent provides continuous automated monitoring that detects issues before they impact production.
Related MCP server: AnomalyArmor
How the MCP Agent Works
What is MCP?
MCP (Model Context Protocol) allows AI assistants (Claude, ChatGPT) to call external tools. Instead of just generating text, the AI can invoke real functions that perform computations.
Agent Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ MCP ML MONITORING AGENT │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ USER / AI ASSISTANT │
│ │ │
│ │ "Is my model still working well?" │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ MCP SERVER (mcp_server.py) │ │
│ │ │ │
│ │ Exposes tools that AI can call: │ │
│ │ - set_reference_data - detect_drift │ │
│ │ - record_predictions - get_performance_report │ │
│ │ - get_health_summary - get_retraining_recommendation │ │
│ └────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────┐ ┌────────────────┐ ┌────────────────────────────┐ │
│ │ DRIFT DETECTOR │ │ PERFORMANCE │ │ ALERT SYSTEM │ │
│ │ │ │ MONITOR │ │ │ │
│ │ - KS-Test │ │ - Accuracy │ │ - Severity Classification │ │
│ │ - PSI Score │ │ - Precision │ │ - Retraining Decisions │ │
│ │ - Wasserstein │ │ - Recall, F1 │ │ - Actionable Suggestions │ │
│ └────────────────┘ └────────────────┘ └────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘How Each Component Works
1. Drift Detector (src/drift_detector.py)
Purpose: Detect when production data differs from training data.
Process:
Training Data (Reference) Production Data (Current)
│ │
└──────────┬───────────────────┘
│
▼
┌─────────────────────┐
│ Statistical Tests │
│ │
│ 1. KS-Test │─── p-value < 0.05? → DRIFT
│ 2. PSI Score │─── PSI > 0.2? → CRITICAL
│ 3. Wasserstein │─── Distance > 0.15? → WARNING
└─────────────────────┘
│
▼
┌─────────────────────┐
│ Per-Feature Report │
│ │
│ feature_1: OK │
│ feature_2: DRIFT │
│ feature_3: DRIFT │
└─────────────────────┘Statistical Methods:
Method | What It Measures | Formula |
KS-Test | Whether two samples come from same distribution | Max difference between cumulative distributions |
PSI | Magnitude of distribution shift | Σ (current - reference) × ln(current/reference) |
Wasserstein | Minimum "work" to transform one distribution to another | Earth Mover's Distance |
2. Performance Monitor (src/performance_monitor.py)
Purpose: Track model accuracy over time and detect degradation.
Process:
Model Predictions Ground Truth
(y_pred) (y_true)
│ │
└──────────┬───────────────┘
│
▼
┌──────────────────────┐
│ Calculate Metrics │
│ │
│ Accuracy = 0.92 │
│ Precision = 0.89 │
│ Recall = 0.91 │
│ F1-Score = 0.90 │
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Compare to Baseline │
│ │
│ Baseline: 0.95 │
│ Current: 0.92 │
│ Delta: -0.03 │──── Drop > 5%? → WARNING
│ │──── Drop > 10%? → CRITICAL
└──────────────────────┘3. Alert System (src/alert_system.py)
Purpose: Generate actionable recommendations based on analysis.
Decision Logic:
Drift Report + Performance Report
│
▼
┌─────────────────────────────────────┐
│ Evaluate Conditions │
│ │
│ IF drift_severity == CRITICAL │
│ OR performance_drop > 10% │
│ THEN urgency = IMMEDIATE │
│ │
│ IF drift_severity == WARNING │
│ OR performance_drop > 5% │
│ THEN urgency = SOON │
│ │
│ ELSE no retraining needed │
└─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Generate Recommendations │
│ │
│ - Collect recent production data │
│ - Validate preprocessing pipeline │
│ - Run A/B test before deployment │
└─────────────────────────────────────┘How LangFlow Works (Advanced Version)
LangFlow provides a visual interface to build the same monitoring pipeline with additional enterprise features.
LangFlow Architecture
┌─────────────────────────────────────────────────────────────────────────────┐
│ LANGFLOW VISUAL PIPELINE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ DATA INGESTION MONITORING INTELLIGENT │
│ ───────────── ────────── RESPONSE │
│ ─────────── │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ CSV │────────────────►│ Drift │──────────────►│ Root │ │
│ │ JSON │ │ Detect │ │ Cause │ │
│ │ API │ └────┬────┘ │ (LLM) │ │
│ └────┬────┘ │ └────┬────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Data │ │ Anomaly │ │ Remedy │ │
│ │ Valid │ │ Detect │ │ Suggest │ │
│ └─────────┘ └────┬────┘ └────┬────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ │
│ │ Perf │ │ Code │ │
│ │ Monitor │ │ Gen │ │
│ └────┬────┘ └────┬────┘ │
│ │ │ │
│ INTEGRATIONS │ │ │
│ ──────────── │ │ │
│ ▼ ▼ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Slack │◄────────────────│ Alert │◄──────────────│ Ansible │ │
│ │ K8s │ │ System │ │ Playbook│ │
│ │ GitHub │ └─────────┘ └─────────┘ │
│ │ Prom │ │
│ └─────────┘ │
│ │
│ ADVANCED FEATURES │
│ ───────────────── │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Multi-Model │ │ A/B Testing │ │ Auto │ │ Federated │ │
│ │ Comparison │ │ Coordinator │ │ Retraining │ │ Learning │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘LangFlow Component Details
Data Ingestion Components
Component | Purpose | How It Works |
CSV Ingestion | Load CSV files | Parses file, infers dtypes, returns DataFrame |
API Ingestion | Fetch from REST API | Makes HTTP request, extracts data from JSON path |
Stream Ingestion | Handle streaming | Buffers records, outputs windows when size reached |
Data Validator | Check data quality | Validates schema, required columns, value ranges |
Monitoring Components
Component | Purpose | How It Works |
Drift Detection | Compare distributions | Runs KS-test, PSI, Wasserstein on each feature |
Anomaly Detection | Find outliers | Trains Isolation Forest, marks anomalous samples |
Performance Monitor | Track metrics | Calculates accuracy/F1, compares to baseline |
Time Series Forecast | Predict trends | Uses exponential smoothing to forecast metrics |
Intelligent Response Components
Component | Purpose | How It Works |
Root Cause Analysis | Diagnose issues | Sends drift + performance data to LLM for analysis |
Remedy Suggestion | Recommend fixes | Maps severity to action templates |
Code Generator | Create fix scripts | Generates Python retraining code from templates |
Ansible Playbook | Automate rollback | Creates K8s deployment/rollback playbooks |
SHAP Explainer | Model explainability | Calculates SHAP values, ranks feature importance |
Integration Components
Component | Purpose | How It Works |
Kubernetes | Manage deployments | Uses K8s API to scale, rollback deployments |
Prometheus | Push metrics | Sends metrics to Pushgateway for Grafana |
Slack | Send alerts | Posts formatted messages to Slack channels |
GitHub | Create issues | Opens issues with drift/performance details |
Advanced Features
Component | Purpose | How It Works |
Multi-Model Compare | Compare versions | Ranks models by accuracy, latency, drift score |
A/B Testing | Statistical testing | Runs t-test, calculates Cohen's d for significance |
Auto Retraining | Trigger retraining | Evaluates thresholds, initiates pipeline if exceeded |
Federated Learning | Distributed training | Coordinates model updates across nodes, aggregates with FedAvg |
Output Validation
The monitoring_report.json output is correct. Here's what it shows:
Drift Analysis:
├── Total Features: 20
├── Drifted Features: 5 (25%)
├── Severity: CRITICAL
└── Affected: feature_3, feature_5, feature_7, feature_10, feature_16
Performance:
├── Current Accuracy: 0.974
├── Baseline Accuracy: 0.937
├── Delta: +0.037 (improving)
└── Status: HEALTHY
Recommendation:
├── Should Retrain: YES
├── Urgency: IMMEDIATE
└── Reason: Critical drift in 5 featuresKey Insight: Even though performance improved (+3.7%), the system correctly flags CRITICAL status because 25% of features have drifted significantly (PSI > 0.2). This is important because:
Current performance may be misleading (test data still similar to training)
Future predictions on truly drifted data will degrade
Proactive retraining prevents future failures
Quick Start
cd mcp-ml-monitor
pip install -r requirements.txt
python demo.pyProject Structure
mcp-ml-monitor/
├── src/ # Core MCP Agent
│ ├── drift_detector.py # Statistical drift detection
│ ├── performance_monitor.py # Metric tracking
│ ├── alert_system.py # Recommendation engine
│ └── mcp_server.py # MCP protocol server
│
├── langflow/ # Advanced LangFlow Version
│ ├── components/
│ │ ├── data_ingestion.py # CSV, API, Streaming
│ │ ├── ml_monitoring.py # Drift, Anomaly, Forecast
│ │ ├── intelligent_response.py # LLM, Code Gen
│ │ ├── integrations.py # Slack, K8s, GitHub
│ │ └── advanced_features.py # A/B, Federated
│ ├── flows/
│ │ └── ml_monitoring_flow.json # Import to LangFlow
│ └── orchestrator.py # Pipeline runner
│
├── demo.py # Run this for demo
└── requirements.txtThis server cannot be installed
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