glin

CLI tool and Python library that equips AI agents with an instant, statistical "gut feeling" (System 1 thinking).

glin trains an Explainable Boosting Machine on a CSV, then exposes it to LLM agents over MCP — locally over stdio, or remotely over MCP's standard streamable-http transport. Every prediction comes with an exact, zero-approximation breakdown of which features drove it, straight from the model's own additive structure (no SHAP/LIME approximation).

Install

The PyPI package is named glin-ml (the name glin was already taken by an unrelated project) — but the CLI command and Python import are both just glin.

uv tool install glin-ml     # recommended: installs the `glin` command in an isolated env

pipx install glin-ml        # equivalent, if you use pipx instead of uv

pip install glin-ml         # plain pip also works, into whatever environment is active

Either way, you get the glin command:

glin --help

For development (editable install from a clone of this repo):

git clone https://github.com/AkashChatterjee/glin.git
cd glin
pip install -e ".[dev]"

Train a model

glin train path/to/data.csv --target churn --name churn_v1

Models are saved under ~/.glin/models/<name>/.

glin list

glin delete churn_v1        # prompts for confirmation
glin delete churn_v1 --yes  # skips the prompt

Use it locally (Claude Desktop, Cursor, ...)

Add to your MCP client's config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "glin": {
      "command": "glin",
      "args": ["serve", "--mode", "stdio"]
    }
  }
}

Deploy it remotely (e.g. one EC2 box, any MCP-aware agent)

docker build -t glin .
docker run -p 8000:8000 -v ~/.glin:/root/.glin glin

Then point any MCP client at the standard streamable-http endpoint:

claude mcp add --transport http glin http://<host>:8000/mcp

Tools exposed over MCP

• list_models() — all trained models available.
• inspect_model(model_name) — feature schema and target classes.
• predict(model_name, features, top_n=10) — predicted class and probabilities, plus the full glassbox audit: base rate, every term's contribution (sorted by magnitude), and an explicit additivity check against the model's own predicted probability.

Data requirements

glin train validates your CSV before doing any work — hard problems (e.g. a target column with only one class) stop training with a clear error; soft issues (e.g. a date-like column) print a warning and training proceeds anyway. These rules live in glin/validation.py as a flat, appendable list, so support for a currently-unsupported shape below can be added by adding one rule and one preprocessing case, without touching the rest of the pipeline.

Feature columns — supported today:

Data shape	What happens
Numeric (int/float), including NaN	Passed through as-is; EBM natively bins missing values into their own split.
Dirty numeric strings (blanks, e.g. " " for a new customer's total charges)	Coerced to float64 if ≥80% of non-null values parse as numbers; unparseable cells become NaN.
Categorical strings, any cardinality up to 250 unique values	Standardized (lowercased, stripped) and one-hot-style binned by EBM; missing/unseen values map to a __missing__ sentinel.
Boolean columns	Treated as a 0/1 continuous numeric feature.

Feature columns — not yet supported (each is flagged by glin train's validator when detected):

Data shape	What actually happens	Why
Dates / timestamps	No date features are extracted. The column is either dropped (if high-cardinality) or kept as a meaningless categorical label — no time-based signal survives either way.	Cut from V1 scope; a real dataset need should drive adding cyclical date-feature extraction.
Free text / natural language	Dropped once it exceeds 250 unique values; below that threshold it becomes a set of (almost certainly useless) categorical labels.	glin doesn't do NLP; a text column isn't a set of classes.
Currency symbols / thousands separators ($, €, £, ,)	Not stripped. "$1,234.56" fails the ≥80% numeric-parse threshold and falls back to categorical (i.e. garbage). Clean these before training.	Cut from V1 scope under an "assume a healthy dataset" simplification.
Lists / dicts / nested JSON in a cell	Not parsed structurally; treated as an opaque string.	No structured extraction implemented.
Row identifiers (sequential IDs, UUIDs, hashes)	Dropped intentionally — not a gap, this is by design.	IDs carry no predictive signal.

Target column requirements:

• At least 2 distinct non-null values (binary or multiclass) — a single-class target is a hard error.
• Rows with a missing target value are dropped automatically (with a warning); the rest are unaffected.
• A numeric target with many distinct values (>20) triggers a warning that it looks like a regression target — glin trains classifiers, not regressors.

Releasing (maintainers)

PyPI rejects re-uploading a version number, so bump version in pyproject.toml first and double check the metadata (description, classifiers, URLs) before publishing — there's no fixing a released version after the fact, only shipping a new one.

rm -rf dist
uv build

Dry run against TestPyPI first:

twine upload --repository testpypi dist/*
# username: __token__, password: a TestPyPI API token

Verify it actually installs from there before touching the real index:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ glin-ml

Then publish for real:

twine upload dist/*
# username: __token__, password: a PyPI API token (separate account/token from TestPyPI)

Confirm the public release works with a clean install:

uv tool install glin-ml
glin train some.csv --target y --name smoke_check