Qualify dataset columns and process them against a target with AutoCarver, fully on your machine.
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Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-mdefrance-autocarver": {
"args": [
"autocarver"
],
"command": "uvx"
}
}
}Are you the author?
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Qualify dataset columns and process them against a target with AutoCarver, fully on your machine.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'autocarver' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked autocarver against OSV.dev.
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AutoCarver turns raw numeric, categorical, and ordinal columns into optimal, drift-robust, human-readable bins in a few lines of code. Stop losing model performance to suboptimal manual binning — and stop discovering overfit bins in production monitoring.
min_freq, max_n_mod and metric (Tschuprow's T by default, or Cramér's V), no other admissible bin combination scores higher. It checked them all so you don't have to.fit time rather than in monitoring.Features, fit a carver, transform: the whole feature set is carved in one supervised pass, not one notebook per feature. One carver per target type — BinaryCarver, MulticlassCarver, OrdinalCarver, ContinuousCarver (regression) — all with the identical API.On the Titanic quick start, Fare collapses from 72 pre-carving modalities to 2 bins while its association with survival rises: Tschuprow's T 0.18 raw → 0.29 carved.
Built for credit scoring, fraud detection, and risk modeling.
📊 Cross-validated robustness. fit now accepts a cv argument for extra
held-out robustness views on top of (or instead of) a dev set:
carver.fit(X, y, cv=5). Accepts an int, any scikit-learn splitter, or
explicit index pairs, resolved via sklearn.model_selection.check_cv — folds
veto over-fit combinations but never reorder them (ranks stay anchored to the
full train set). See Cross-validation folds.
🤖 LLM & MCP integration. AutoCarver now ships a local Model Context Protocol server: point an MCP-aware assistant (VS Code Copilot, Claude Desktop, Cursor, …) at a data file and let it qualify the columns and carve them against your target through tool calls. The server runs fully on your machine — your dataset is never sent to AutoCarver or any external service (on