Data Analytics MCP 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