key-drivers-mcp

from petrmasa

MCP server for key driver and feature importance analysis based on rule mining

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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.

Tools

Tool	Purpose
`load_dataset`	Load a CSV file into session memory
`list_datasets`	List all loaded datasets
`find_drivers`	Find key drivers of a target outcome
`explain_segment`	Driver analysis conditioned on a segment variable (CLARA)

Examples

Titanic — what drove survival?

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

Baseline survival rate: 38.4%

Driver	Survival rate	vs Baseline
Sex: female	74.2%	1.9× higher
Sex: male	18.9%	2.0× lower
Low fare ≤ £10.50	20.9%	1.8× lower
Deck D	75.8%	2.0× higher

Sub-drivers are returned automatically. Within male passengers, 1st class men recovered to 36.9% — nearly double the male average. Within low-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 shows sex as the top driver (women 74.2%, men 18.9%). Sub-drivers within women immediately reveal that 3rd class women dropped to 50% — a coin flip, far below the female average. That triggers a follow-up with filters={"sex": "female", "pclass": "3"}:

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

Driver	Survival rate	vs Segment baseline
Embarked at Queenstown	72.7%	1.5× higher
Fare £6.75–£7.77	72.4%	1.4× higher
Embarked at Southampton	37.5%	1.3× lower

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?"

Baseline: 70% good credit rating

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

Profile	Good credit rate	vs Baseline
Overdrawn checking account	50.7%	1.4× lower
Overdrawn + loan duration > 24 months	34.4%	2.0× lower
Overdrawn + critical credit history	73.1%	back to baseline
Long loan duration > 30 months	52.0%	1.3× lower
Long loan + no property	38.9%	1.8× lower
Long loan + no checking account	79.3%	1.1× higher

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?"

Baseline: 34.9% positive

Driver	Probability	vs Baseline
Glucose > 147 mg/dL	74.3%	2.1× higher
Glucose > 147 + age 27–33	88.5%	2.5× higher
Glucose > 147 + BMI 33.7–37.8	84.2%	2.4× higher
Glucose > 147 + many pregnancies (>7)	85.7%	2.5× higher
Glucose ≤ 109 mg/dL	14.0%	2.5× lower
Age ≤ 23	13.3%	2.6× lower

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?"

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

Profile	>50K rate	vs Women's baseline
Doctorate	56.6%	5.2× higher
Prof-school	47.7%	4.4× higher
Doctorate + married	88.0%	8.1× higher
Prof-school + married	84.2%	7.7× higher
Prof-school + never-married	35.7%	3.3× higher
Own-child relationship	1.2%	9.0× lower

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.

How it works

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.

Configuration

Add this to your MCP client config (e.g. Claude Code .mcp.json). No installation needed — uvx fetches and runs the package automatically.

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

No uv? Install it with pip install uv, or use pipx install key-drivers-mcp and set "command": "key-drivers-mcp" instead.

Requirements

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