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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
load_modelA

Load a model file and instantiate the appropriate SHAP explainer.

Note: Prompt the user to provide local filesystem paths on their machine, public URLs, or upload via http://localhost:8765/ui/. Files attached directly in chat are stored in a cloud container (/mnt/user-data/) that local tools cannot reach.

Parameters

model_path : str | None Path to a local .joblib or .pkl model file. model_url : str | None Public URL to download a model file. model_type : str One of 'tree', 'linear', 'deep', 'kernel'. Default is 'tree'. background_path : str | None Path to background CSV dataset (optional for tree/kernel, required for deep).

run_analysisB

Run SHAP explainer against a dataset.

Note: Ask the user for local file paths, public URLs (data_url), or use the Web GUI at http://localhost:8765/ui/. For small CSVs (< 500 rows), you may read the table from chat and pass the rows directly in data.

Parameters

data_path : str | None Path to a local CSV file. data_url : str | None Public URL to download a CSV dataset file. data : list[dict] | None Inline dataset passed as a JSON array. sample_size : int | None Override default auto-cap for dataset row sampling.

get_feature_importanceA

Return global feature importance from stored SHAP values.

Parameters

top_n : int Number of top features to return (default: 10).

explain_predictionC

Return SHAP breakdown for a single instance.

Parameters

index : int | None Row index in the analyzed dataset. data : dict[str, Any] | None Inline feature dictionary for explaining an arbitrary instance.

get_interactionB

Return SHAP interaction values between two features (Tree models only).

Parameters

feature_a : str Name of the first feature. feature_b : str Name of the second feature.

get_plotA

Generate a SHAP visualization, save it as a PNG file, and return file path and URL.

Parameters

plot_type : str One of 'summary', 'bar', 'waterfall', 'force', 'dependence', 'heatmap'. index : int | None Row index in dataset (required for waterfall and force). feature_name : str | None Feature name (required for dependence). top_n : int Max features to show (default: 10). color_feature : str | None Feature to color by for dependence plots. output_path : str | None Override default output directory path.

Prompts

Interactive templates invoked by user choice

NameDescription
setup_analysisGuide analysis setup with model type inference and sensible defaults.
interpret_resultsStructure factual interpretation of SHAP feature importance results.

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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