MLOps Model Context Protocol (MCP) Server

A production-grade Python Model Context Protocol (MCP) server built with the official mcp.server.fastmcp SDK, designed for local offline Machine Learning operations, dataset profiling, dynamic classification training, hyperparameter optimization, and evaluation metric visualization.

Key Architectural Principles

1. Protocol Standards: Implements the Model Context Protocol (MCP) over standard input/output (stdio) transport.

2. Stdout Framing Isolation: Standard output (sys.stdout) is strictly reserved for JSON-RPC 2.0 frames (tools/call, tools/list, etc.). Zero print() statements are allowed.

3. Stderr Operational Logging: All debugging information, training progress, and tracebacks are routed strictly to sys.stderr via Python's standard logging and direct stream flushes.

4. 100% Offline Execution: Operates on local files using pandas, numpy, and scikit-learn without external APIs.

Related MCP server: mcp-server

Implemented Tools Ecosystem (9 Production MLOps Tools)

Quickstart Setup

1. Create and Activate Virtual Environment

python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install Dependencies

pip install -r requirements.txt

3. Test with MCP Inspector

Inspect and interactively call the tools using the official MCP CLI:

npx @modelcontextprotocol/inspector python ml_server.py

4. Configure in VS Code, Cursor, or Claude Desktop

A. VS Code (Cline / Roo Code / Continue)

1. Install Cline or Roo Code from the VS Code Extensions Marketplace (Ctrl+Shift+X / Cmd+Shift+X).

2. Open Cline settings > MCP Servers > Configure MCP Servers (cline_mcp_settings.json).

3. Add the server entry:

{
  "mcpServers": {
    "mlops-engine": {
      "command": "/ABSOLUTE/PATH/TO/.venv/bin/python",
      "args": [
        "/ABSOLUTE/PATH/TO/ml_server.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      },
      "disabled": false
    }
  }
}

(On Windows, use .venv\Scripts\python.exe with double backslashes \\).

B. Cursor IDE

1. Go to Settings (Cmd+, or Ctrl+,) > Features > MCP.

2. Click + Add New MCP Server.

3. Set:

   • Name: mlops-server

   • Type: command

   • Command: /ABSOLUTE/PATH/TO/.venv/bin/python /ABSOLUTE/PATH/TO/ml_server.py

4. Click Save. The status dot will turn green.

C. Claude Desktop

Add to your claude_desktop_config.json:

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

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

{
  "mcpServers": {
    "mlops-engine": {
      "command": "python",
      "args": [
        "/absolute/path/to/ml_server.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      }
    }
  }
}

5. Example Prompts to Ask in VS Code / Cursor

• "Profile and clean 'sample_dataset.csv', impute any missing values, and report statistics."

• "Train a Random Forest classifier on 'sample_dataset.csv' predicting 'churn' with 80/20 train/test split."

• "Run 3-fold cross-validated hyperparameter optimization for tree depth and estimators."

• "Train an Artificial Neural Network on 'sample_dataset.csv' for 30 epochs and output test accuracy."

• "Generate a 300-DPI confusion matrix heatmap and export an HTML executive report."

• "Synthesize a ready-to-run interactive Streamlit web dashboard 'app.py' for customer churn."

6. Running Generated Artifacts

• Streamlit Web Dashboard: Run streamlit run app.py to open the interactive UI in your browser.

• Standalone HTML Report: Open ml_report.html in any web browser to view embedded visualizations and metrics.

• Confusion Matrix: View confusion_matrix.png directly in VS Code.