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Documentation Index

Fetch the complete documentation index at: https://docs.summand.com/llms.txt

Use this file to discover all available pages before exploring further.

All tools are read-only. Most of them take a datasetId of the form ds_019…. Use list_datasets to discover IDs.

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list_datasets

List every dataset the caller can see — owned datasets plus anything shared with them. Parameters: none. Returns: an array of { id, name, kind, status, targetColumn, latestVersion, accessLevel, updatedAt } plus a count.
“List my Summand datasets.”

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describe_dataset

Full metadata for a dataset, including its semantic layer manifest. The semanticLayer.manifest.components field tells you which components are valid inputs to get_columns and get_semantic_data (e.g. column_stats, ebm_graphs, feature_metadata, umap_embedding).
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datasetId
string
required
The dataset ID (e.g. ds_019abc…).
“Describe ds_019abc… — what analysis components are available?“

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get_columns

Column information for a dataset. Returns rich statistics from the column_stats component when available (type, mean, min, max, null count, distinct count), or falls back to feature names from feature_metadata.
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datasetId
string
required
The dataset ID.
“What columns does ds_019abc… have, and what are their distributions?“

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get_semantic_data

Retrieve a specific semantic-layer component. This is the workhorse tool — call describe_dataset first to see what’s available, then pull the component you want.
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datasetId
string
required
The dataset ID.
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component
string
required
Component name from the manifest. Common values: column_stats, ebm_graphs, feature_metadata, umap_embedding, data_quality.
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params
object
Optional filter params. For example, pass { "feature": "price" } with ebm_graphs to retrieve only that feature’s contribution data instead of the full graph payload.
“Get the EBM graphs for JobLevel in ds_019abc….”

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get_surprises

Surprise findings for a dataset — ML-generated unexpected statistical patterns with confidence scores and natural-language explanations. Returned in display order, with deleted items filtered out.
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datasetId
string
required
The dataset ID.
“What are the surprising findings in my HR attrition dataset?“

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compare_schemas

Side-by-side column comparison between two datasets. Returns columns in both, columns only in A, and columns only in B.
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datasetIdA
string
required
First dataset ID.
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datasetIdB
string
required
Second dataset ID.
“Compare the schemas of ds_019abc… and ds_019def….”

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Typical workflow

1

Discover

list_datasets → pick a dataset ID.
2

Inspect

describe_dataset → check status and read semanticLayer.manifest.components.
3

Drill in

get_columns, get_surprises, or get_semantic_data with a specific component.