Kaggle Dataset Analyst — MCP Server

An MCP server that exposes tools, resources, and prompts for exploratory data analysis (EDA) and machine learning on Kaggle-style CSV datasets. Built with Python, Pandas, scikit-learn, matplotlib, and the MCP Python SDK.

A capstone-level project that touches most of the data skills employers look for: MCP, Python, Pandas, statistics, EDA, scikit-learn, prompt engineering, data visualization, feature engineering, and model evaluation.

What it does

Tools (model-invoked actions)

Resources (read-only context)

• datasets://list — newline list of available datasets
• dataset://{filename}/schema — JSON schema (columns, dtypes, missing counts)

Prompts (reusable workflows)

• eda_walkthrough — a guided EDA plan for a dataset
• ml_pipeline — an end-to-end modelling plan for a target column

Project structure

kaggle_mcp/
├── mcp_server.py        # the MCP server (tools, resources, prompts)
├── smoke_test.py        # calls every tool in-process (logic check)
├── client_test.py       # full MCP client <-> server round-trip (protocol check)
├── main.py              # convenience launcher (same as `uv run mcp_server.py`)
├── datasets/
│   └── train.csv        # Titanic dataset (891 rows)
├── models/              # saved trained models (.joblib)
├── outputs/             # generated plots (.png)
├── prompts/             # (room for saved prompt templates)
├── pyproject.toml
└── README.md

Setup

# from the kaggle_mcp/ directory
uv sync          # installs dependencies into .venv

Mental model. The server never runs on its own — it speaks the MCP protocol over stdio and waits for a client to drive it. There are two ways to be that client:

>

- Level 1 — MCP Inspector: a web UI where you click tools by hand. Best for learning and debugging the server. - Level 2 — Claude (Desktop or Code): the AI is the client and calls the tools for you from a normal chat. This is the real, day-to-day way to use it.

>

Get comfortable in the Inspector first, then graduate to Claude.

Level 1 — Drive the server with the MCP Inspector

The Inspector is a browser UI that connects to your server and lets you invoke each tool by hand.

One-time prerequisites (already installed if you ran uv sync):

• The cli extra of the MCP SDK — uv add "mcp[cli]" (provides the mcp dev

command). It is declared in pyproject.toml, so uv sync installs it.

• Node.js — the Inspector UI is a Node app launched via npx.

Launch it:

uv run mcp dev mcp_server.py

It prints a token-prefilled URL like http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=.... Open that link (use the one with the token — the Inspector requires it). The first launch may pause while npx downloads the Inspector. A green ● Connected dot means you're live. Press Ctrl+C in the terminal to stop.

Workflow — run a tool:

1. Click the Tools tab → List Tools (you should see all 10 tools).
2. Click a tool, e.g. `profile_dataset`.
3. Fill in its arguments — for profile_dataset, set filename = train.csv.
4. Click Run Tool. The JSON result appears on the right.

A good first session (Titanic):

train_model actually fits a RandomForest — give it a few seconds.

Other tabs:

• Resources → List Resources shows datasets://list; resource

templates like dataset://{filename}/schema are filled in with a filename.

• Prompts are reusable instruction templates — clicking Get Prompt

returns text (e.g. eda_walkthrough) meant to be handed to an AI. They don't execute anything themselves; the real work is in Tools.

The red "Error output from MCP server" panel is not errors — the Inspector labels everything the server prints to stderr that way. Lines like INFO Processing request of type ... are normal activity logs.

Level 2 — Use the server through Claude (real usage)

Here Claude is the client: you chat normally and it decides which tools to call.

Claude Code (CLI)

claude mcp add kaggle-analyst -- uv --directory c:\dev\kaggle_mcp_project\kaggle_mcp run mcp_server.py

Claude Desktop

Add this to your claude_desktop_config.json (Settings → Developer → Edit Config), then restart Claude Desktop:

{
  "mcpServers": {
    "kaggle-analyst": {
      "command": "uv",
      "args": [
        "--directory",
        "c:\\dev\\kaggle_mcp_project\\kaggle_mcp",
        "run",
        "mcp_server.py"
      ]
    }
  }
}

Then just ask

"Profile train.csv, tell me which columns have missing data, then train a model to predict Survived and report the most important features."

Claude will call profile_dataset → detect_missing_values → train_model on its own and summarize the results — the same tools you clicked in the Inspector, now driven by the AI.

Making predictions with a trained model

Once train_model has saved a model, the predict tool scores new rows — you supply whatever feature values you want and it returns the predicted target (plus a confidence for classifiers). The saved model is a full pipeline, so it handles missing values and categorical columns for you; you only provide the feature columns used in training.

Two input modes:

• Ad-hoc rows — pass records, a list of feature dicts you make up:

  predict(
    model="train_Survived_classification",
    records=[{"Pclass": 1, "Sex": "female", "Age": 38, "Fare": 71.3, "Embarked": "C"}]
  )
  # -> {"Survived": 1, "confidence": 1.0}

• A whole CSV (e.g. a Kaggle test.csv) — pass filename, optionally echo

an id column and write a submission CSV:

  predict(
    model="train_Survived_classification",
    filename="test.csv",
    id_column="PassengerId",
    save_csv=True   # writes outputs/<model>_predictions.csv
  )

In Claude, just describe the case — it fills in the records for you:

"Predict survival for a 28-year-old man in 3rd class who paid £8 and boarded at Southampton."

Use list_models to see which saved models are available and what each predicts.

Automated checks (no UI)

Two scripts verify the server without the Inspector — handy for a quick sanity check or CI:

uv run python smoke_test.py     # calls every tool in-process (logic check)
uv run python client_test.py    # full MCP client <-> server round-trip (protocol check)

client_test.py exercises the exact stdio path a real client uses, so prefer it when confirming the server actually works end-to-end.

Using your own Kaggle data

1. Drop any .csv into datasets/, or
2. Configure Kaggle credentials (KAGGLE_USERNAME / KAGGLE_KEY, or

~/.kaggle/kaggle.json) and call the download_kaggle_dataset tool with a slug like yasserh/titanic-dataset.

Every tool takes a filename argument, so the server works with any dataset you add — not just Titanic.

Troubleshooting

`Invalid JSON` / `EOF while parsing` after running `uv run mcp_server.py`. Expected. The server is waiting for MCP protocol messages on stdin; anything you type by hand is rejected as malformed. Press Ctrl+C and use client_test.py or an MCP client instead.

`Error: typer is required. Install with 'pip install mcp[cli]'` from `uv run mcp dev`. The MCP SDK was installed without its cli extra. Fix:

uv add "mcp[cli]"

(You do not need to activate .venv — uv run/uv add already use it.)

`Failed to spawn mcp` / `os error 4551` from `uv run mcp dev`. Windows Smart App Control is blocking the unsigned mcp.exe helper. The mcp dev Inspector is optional. Use uv run python client_test.py to verify the server without it, or disable Smart App Control (Settings → Privacy & security → Windows Security → App & browser control → Smart App Control).

The Inspector's red "Error output from MCP server" panel is full of lines. Not an error — the Inspector shows everything the server logs to stderr there. INFO Processing request of type ... lines are normal.

Implementation notes

• Heavy imports are at module scope, not lazy. FastMCP runs synchronous tool

functions in a worker thread, and a first-time import sklearn from a non-main thread can deadlock on CPython's import lock on Windows. So scikit-learn / joblib / kagglehub are imported once at startup on the main thread. (A purely in-process test like smoke_test.py won't catch this — only the real stdio path in client_test.py does.)

• `train_model` uses `n_jobs=1`. As a stdio server this process has its

stdin/stdout redirected to pipes; parallel joblib/loky workers would inherit those handles and can deadlock on Windows. RandomForest on tutorial-sized data is fast enough single-threaded.

• Path-traversal guard. Every dataset access is resolved safely inside

datasets/, so a filename like ../../secret.csv is refused.