How do I use Data Analytics MCP Toolkit?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Data Analytics MCP Toolkit Generate a bar chart of monthly sales from the sales.csv file" 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.

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