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

pip install -e .

Train a model

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

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

glin list

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:

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

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.