Data Analytics Toolkit

An MCP (Model Context Protocol) server that exposes data visualization and simple machine learning tools. When an external LLM calls the toolkit, it can use the high-level run_analytics tool to describe intent and data; the server selects and runs the appropriate pipeline (visualization or ML) and returns charts or metrics.

Features

• Data: load_data (CSV/JSON string or URL), clean_data (drop NA, optional normalize)
• Visualization: plot_bar, plot_line, plot_scatter, plot_histogram, plot_box, plot_heatmap (return base64 PNG)
• ML: train_test_split, train_linear_regression, train_logistic_regression, train_kmeans, plus evaluate_regression, evaluate_classification, evaluate_clustering
• Pipeline: run_analytics(intent, data_source) — intent-based routing to the right pipeline

Install

cd /path/to/trying_IBM_MCP
pip install -e .
# or
pip install -r requirements.txt

From the project root, ensure src is on PYTHONPATH when running the server (or install in editable mode).

Run the MCP server

stdio (for Cursor / IDE):

# From project root, with src on path
PYTHONPATH=src python -m data_analytics_mcp.server

Or with uv:

uv run --project . python -m data_analytics_mcp.server

(If using a pyproject.toml that sets packages under src, install first with pip install -e . then run python -m data_analytics_mcp.server from the repo root.)

Cursor MCP configuration

Add the server to Cursor (e.g. in Cursor Settings → MCP, or project .cursor/mcp.json):

{
  "mcpServers": {
    "data-analytics": {
      "command": "python",
      "args": ["-m", "data_analytics_mcp.server"],
      "cwd": "/path/to/trying_IBM_MCP",
      "env": { "PYTHONPATH": "src" }
    }
  }
}

Use the full path for cwd. If you installed the package (pip install -e .), you can use:

{
  "mcpServers": {
    "data-analytics": {
      "command": "python",
      "args": ["-m", "data_analytics_mcp.server"],
      "cwd": "/Users/jerrychen/projects/trying_IBM_MCP"
    }
  }
}

Usage

• One-shot: Call run_analytics with a natural-language intent (e.g. "show distribution of sales", "predict price from square_feet", "cluster into 4 groups") and the data as CSV/JSON string or URL. The server returns either a chart (base64 image) or ML metrics and a short model summary.
• Step-by-step: Use load_data → get data_id → then call clean_data, plot_*, or train_test_split → train_* → evaluate_* as needed. Use resources analytics://pipelines and analytics://pipelines/visualization (etc.) to see pipeline descriptions.

Project layout

src/data_analytics_mcp/
  server.py   # MCP app, tools, resources
  pipeline.py # Intent → pipeline; execute_pipeline
  data.py     # load_data, clean_data
  viz.py      # Plot functions → base64 PNG
  ml.py       # Train/evaluate regression, classification, clustering
  store.py    # In-memory session store