List the distinct customer-service categories in the dataset (e.g. refund, shipping, account). Use this first when the user asks 'what categories exist' or when you need to confirm a valid category name before filtering or counting.

List the distinct intents, optionally restricted to one category. Use this to discover valid intent names (e.g. the refund category contains get_refund, track_refund, check_refund_policy) or to answer 'what is the distribution of intents in the ACCOUNT category' by passing with_counts=true.

Find example records matching a category, intent, and/or keyword. Returns the total number of matches plus a small sample of example rows (never the full set). Use this for 'show me N examples of ...'. To get only a count, prefer count_records.

Count how many records match a category, intent, and/or keyword, as a number and as a percentage of the dataset. This is the counting half of a chain: to answer 'how many refund requests did we get?', pass intent='get_refund'. Returns no rows, so it is cheap and safe for large matches.

Retrieve a representative sample of customer messages and agent responses for a category and/or intent, so you can summarize them. Use this for open-ended questions like 'summarize the FEEDBACK category' or 'how do reps respond to cancellations'. Base your summary only on the returned text; do not invent details.