key-drivers-mcp

MCP server for key driver and feature importance analysis. Load any CSV dataset and ask what drives an outcome — survival, credit default, diagnosis, income — and get a ranked breakdown with sub-driver analysis showing not just which factors matter, but how they combine to amplify or completely reverse each other.

Powered byaraxai(CleverMiner association rule analysis).

{ "mcpServers": { "key-drivers": { "type": "stdio", "command": "uvx", "args": ["key-drivers-mcp"] } } }

Nouv?Install it withpip install uv, or usepipx install key-drivers-mcpand set"command": "key-drivers-mcp"instead.

"What are the key drivers to survive in titanic.csv?"

Sub-drivers are returned automatically. Withinmale passengers, 1st class men recovered to 36.9% — nearly double the male average. Withinlow-fare passengers, women still survived at 60.8% while men reached only 10.7%.

Titanic — drill-down from global to a specific segment

"And within women in 3rd class, what helped survival?"

The global result showssexas the top driver (women 74.2%, men 18.9%). Sub-drivers within women immediately reveal that3rd class women dropped to 50%— a coin flip, far below the female average. That triggers a follow-up withfilters={"sex": "female", "pclass": "3"}:

144 women in 3rd class — segment baseline: 50%

Queenstown passengers (mostly Irish emigrants boarding late in small groups) survived at nearly twice the rate of Southampton passengers — a pattern completely invisible in the global analysis. Each drill-down level answers a narrower question using the previous result as the starting point.

German Credit — how factors combine and reverse each other

"What are the key drivers for good credit?"

An overdrawn checking account drops approval to 50.7% — but the sub-driver analysis shows the outcome depends sharply on what else is true:

The same risk factor (overdrawn account) leads to very different outcomes depending on credit history. Borrowers with no checking account are actually safer on long loans — likely self-employed or asset-wealthy.

Diabetes — combinations push risk above 80%

"What are the key drivers for testing positive for diabetes?"

High glucose is already a strong signal (74%), but combining it with age 27–33, elevated BMI, or high pregnancy count pushes risk above 84%. The tool surfaces these compound profiles in a single call.

Income — education can completely override marital status

"What drives income above $50K for women specifically?"

Usingfilters={"sex": "Female"}— women's baseline: 10.9% (vs 23.9% overall):

Never-married women with a doctorate still reach 35.7% — three times the women's baseline — showing that education fully overrides the marital status penalty. The same inversion appears in the overall dataset: never-married alone → 4.5%, but never-married + Doctorate → 44.3%, almost twice the global baseline.

araxai uses association rule analysis (CleverMiner) to find statistically significant rules that explain why a target class occurs. Each driver rule reports:

- probability— how often the target class occurs in that segment
- vs_global_baseline— lift relative to the whole dataset
- vs_parent_segment— lift relative to the parent rule (for sub-drivers)
- strength—+/-signs indicating rule reliability

Numeric columns are automatically binned into quantiles. The server enriches every top-level driver with a sub-analysis, so compound profiles like "overdrawn + long loan" or "high glucose + age 27–33" are returned in a single call.

- Python 3.11+
- araxai >= 0.3.0
- mcp[cli] >= 1.0.0

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