🔬 DriftScope — Autonomous MLOps & Statistical Diagnostics MCP Server

An open-source, cloud-deployed Model Context Protocol (MCP) server that equips Large Language Models (LLMs) with deterministic statistical computing engines to audit data drift and model degradation in production tabular pipelines.

💡 The Problem

LLMs are exceptional at high-level reasoning, code generation, and root-cause analysis, but notoriously unreliable at precise statistical math. When monitoring machine learning pipelines, asking an LLM to evaluate distribution shift directly leads to severe numerical hallucinations.

DriftScope solves this by bridging the LLM to a dedicated Python analytical engine via the open Model Context Protocol:

• The LLM handles orchestration, triage, hypothesis generation, and incident reporting.

• DriftScope executes deterministic, vector-accelerated hypothesis testing (Two-sample Kolmogorov-Smirnov) and Population Stability Index (PSI) calculations using Polars and SciPy.

Related MCP server: Trustwise MCP Server

🏗️ Architecture

┌────────────────────────────────────────────────────────┐
│                      LLM Host                          │
│         (Claude Desktop / Cursor / Custom Agent)       │
└───────────────────────────┬────────────────────────────┘
                            │ JSON-RPC 2.0
                            ▼ (mcp-remote bridge)
               ┌────────────────────────┐
               │    Internet / HTTPS    │
               └────────────┬───────────┘
                            │ Server-Sent Events (SSE)
                            ▼
┌────────────────────────────────────────────────────────┐
│             DriftScope MCP Server (Render)             │
│  ┌──────────────────────────────────────────────────┐  │
│  │                  FastMCP Router                  │  │
│  └────────┬───────────────────────┬───────────────┬─┘  │
│           │                       │               │    │
│           ▼                       ▼               ▼    │
│    [ Tools Engine ]      [ Resources Hub ]   [ Prompts ]│
│    • check_drift         • standards://      • audit_   │
│    • compute_psi           drift-policy        feature │
│    • generate_mock                                     │
│           │                                            │
│           ▼                                            │
│    [ Data Layer: Polars + SciPy + NumPy ]              │
└────────────────────────────────────────────────────────┘

✨ Features & MCP Primitives

1. 🛠️ Tools (Callable Actions)

• check_feature_drift(baseline_csv, current_csv, feature_column, significance_level): Executes two-sample Kolmogorov-Smirnov (KS) tests to detect continuous covariate shift with rigorous $p$-values.

• compute_psi(baseline_csv, current_csv, feature_column, bins): Computes the Population Stability Index (PSI) with equal-frequency baseline binning and Laplace smoothing to classify shift severity (Stable, Moderate, or Critical).

• generate_mock_datasets(): Synthesizes baseline and drifted production samples on the fly for pipeline verification.

Supports both local cloud file paths and remote public HTTP/HTTPS URLs (AWS S3, GitHub raw, etc.).

2. 📂 Resources (Passive Knowledge)

• standards://drift-policy: Exposes organization-wide MLOps threshold standards (e.g., $p < 0.05$ rejection criteria, PSI warning zones) directly into the agent's context.

3. 📝 Prompts (Reusable Workflows)

• audit_feature: Pre-engineered diagnostic prompt guiding the model through end-to-end drift triage, impact evaluation, and retraining recommendations.

🚀 Live Cloud Deployment

DriftScope is deployed as a live cloud service on Render utilizing the Server-Sent Events (SSE) transport.

• Live SSE Endpoint: https://driftscope-mcp.onrender.com/sse

🔌 Quickstart: Connect to Claude Desktop

You can connect your local Claude Desktop to the live cloud deployment in seconds:

1. Open your Claude Desktop configuration file:

   • Windows: %APPDATA%\Claude\claude_desktop_config.json

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

2. Add driftscope-cloud to your mcpServers object:

{
  "mcpServers": {
    "driftscope-cloud": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://driftscope-mcp.onrender.com/sse"
      ]
    }
  }
}

3. Restart Claude Desktop. The 🔨 hammer icon will appear in the chat interface showing your active tools!

📊 Sample Interaction & Output

Prompt:

"Audit both the 'income' and 'age' features for drift using DriftScope and give me an MLOps summary."

Output:

## MLOps Drift Summary

| Feature | KS Stat | p-value | PSI Score | Verdict |
|---|---|---|---|---|
| income  | 0.3960  | 0.00000 | 0.7060    | 🔴 Critical shift |
| age     | 0.0840  | 0.05910 | 0.0230    | 🟢 Stable |

Attention needed: income
- Both the KS test and PSI confirm income has drifted severely (p-value ≈ 0, PSI = 0.706 > 0.20 threshold).
- Recommendation: Upstream investigation required; trigger model retraining fallback pipeline.

💻 Local Development

Clone the repository and run locally using uv:

git clone https://github.com/Aymenrahmanii/driftscope-mcp.git
cd driftscope-mcp

# Install dependencies
uv sync

# Launch the interactive MCP Inspector UI
uv run mcp dev server.py

📦 Tech Stack

• Protocol: Model Context Protocol (MCP) Python SDK

• Framework: FastMCP (Starlette, Uvicorn, SSE)

• Data & Math: Polars, NumPy, SciPy, Scikit-learn, HTTPX

• Infrastructure: Render Web Services, GitHub