Tenzo

Get the day-level values of one target set (from list_targets), optionally filtered by date range (YYYY-MM-DD) and location_id. target_set_id and location_id are STRINGS — pass them exactly as returned by list_targets (ids exceed safe integer precision in JavaScript, so they must never be handled as numbers). Rows have date, location_id, location_name, dimensions (the target's custom breakdown values, e.g. {"day_part": "Lunch"}) and value. Values are always stored per day: total a "sum" target by adding values; for a "mean" target average them (see aggregation on list_targets). Use this to answer questions about the target itself ("what's my sales target this month?") or to compare against actuals fetched with query_metrics over the same dates. To persist a card comparing actuals vs target, use create_card with target_set_id instead. Pass the business_id from your most recent get_analytics_context call.

get_target_data

Get the canonical values Tenzo stores for one or more target dimensions, so you can map a user's spreadsheet cell values onto them. dimension_keys: a list of keys from get_target_dimensions (e.g. ["day_part"]). Returns {dim_key: [canonical values]} (e.g. {"day_part": ["Breakfast", "Lunch", "Dinner"]}). Map each user value (e.g. "Morning") to the closest canonical value (e.g. "Breakfast"); if there is no clear match, ask the user — never invent one. load_target rejects values not in this set. Pass the business_id from your most recent get_analytics_context call.

get_target_dimension_values

List the dimensions a target can be broken down by for this business. Each entry has a `key` (use in load_target's `dimensions` and in each value row's `dimensions`) and a `display_name`. Use this to map a user's spreadsheet dimension column header (e.g. "Daypart") onto a Tenzo dimension key (e.g. "day_part"), then call get_target_dimension_values to map the cell values. Only use a key returned here; if a column has no clear match, ask the user rather than guessing. Pass the business_id from your most recent get_analytics_context call.

get_target_dimensions

List the metrics you can set a target/budget on for this business. Each entry has `key` (use as load_target's metric_key), `display_name`, default `distribution`, `formatter`, and an `available` flag. Use this to map a user's value column (e.g. "Net Sales") onto a Tenzo metric_key (e.g. "sales") before calling load_target — only use a key returned here. This is the curated TARGET metric set; it differs from get_available_metrics (which lists query metrics). Pass the business_id from your most recent get_analytics_context call.

get_target_metrics

List every business the authenticated user can access. Each entry has a numeric `value` (business_id) and a human-readable `label`. The response also carries the currently-active business_id and business_name at the top level — use these in subsequent tool calls.

list_businesses

List logbook templates with their associated locations. Pass the business_id from your most recent get_analytics_context call.

list_logbooks

List manager logs, optionally filtered by location, logbook, author, and date range. logbook_id is a STRING — keep it as one (ids exceed safe integer precision in JavaScript). To pull answers to specific questions, call list_logbooks first and pass exact section titles as `section` and field names as `field`; an answer matching any of them is kept, and full_text then contains only matching answers. exclude_blank drops unanswered fields. Pass the business_id from your most recent get_analytics_context call.

list_logs

List customer reviews, optionally filtered by location and date range. Pass the business_id from your most recent get_analytics_context call.

list_reviews

List the business's performance targets (also called budgets): named sets of target values for one metric (e.g. a sales budget), stored per day and location. Call this whenever the user mentions targets, budgets, goals, or asks how they are doing "vs target" / "vs budget". Returns empty results when the business has no targets. Each target set in `results` includes: - id: a STRING — keep it as one (ids exceed safe integer precision in JavaScript). Pass it as create_card's target_set_id to put the target on a card, or as get_target_data's target_set_id. - target_metric: the column name of the target values (e.g. "target_sales"). Valid in create_card's metrics array and in calculated-column formulas — but ONLY when target_set_id is also set. NOT valid in query_metrics. - actuals_formula: the actual metric being targeted, as a formula over card columns (e.g. "sales", or "sales / ticket_count" for ATV). - suggested_delta_formula: a ready-made "% vs target" calculated-column formula comparing actuals to target. - aggregation: how target values combine over days/locations — "sum" (amount, e.g. sales) or "mean" (a level to hold, e.g. a ratio). - start_date / end_date: the first and last day the target covers. Outside it the target columns are empty, so keep card date filters within this range. - dimensions: extra breakdowns the target was uploaded with (e.g. {"day_part": "Day part"}). Empty {} means location/date only. - locations: the locations the target applies to. To read the raw target numbers, call get_target_data with the target set's id. Pass the business_id from your most recent get_analytics_context call.

list_targets

Create one or more performance targets (a.k.a. budgets) with their values and build the comparison card(s). Use this when the user asks to set, load, upload, or import a target/budget — often from an attached CSV/XLSX or pasted table. You provide values at the target's INTERVAL granularity (one number per bucket × location × dimension-combo, e.g. one per week); the server distributes them to days, registers the target, and builds the card. BEFORE calling, resolve every label in the user's data to Tenzo's canonical identifiers — Core matches EXACTLY and rejects unknown labels: - location → location_id: get_locations, map the user's name (e.g. "london") to the right id (e.g. "1. London"). Pass ids as STRINGS. - value column → metric_key: get_target_metrics (e.g. "Net Sales" → "sales"). - dimension header → dim_key: get_target_dimensions (e.g. "Daypart" → "day_part"). - dimension cell → canonical value: get_target_dimension_values (e.g. "Morning" → "Breakfast"). If a label has no confident match, ASK the user — never invent one. Then echo a short mapping + totals summary and confirm before calling.

load_target

Query a single business metric over a period, optionally grouped by dimensions and filtered. Pass the business_id from your most recent get_analytics_context call. `period` is required. For a relative range pass a `code` from get_analytics_contex (e.g. "WTD", "L7D", "MTD", "YTD"). The server resolves it to the current dates, so never compute or reuse dates yourself. For an explicit calendar range the user gives, pass period="custom" with start_date and end_date (YYYY-MM-DD). Each filter is {"field": <a field from get_available_filters>, "operator": <operator>, "value": <a value, or a list of values for in/nin>}. When query_info.fields is present, label each column with its display_name. If the response contains 0 rows, check query_info.filters_applied to verify your filter values are correct. Common causes of empty results: - A filter value that doesn't match any data (e.g. wrong location name). Try querying with fewer filters or without filters to discover valid values. - A date range with no activity. Do NOT assume a 0-row result means a dimension/filter combination is unsupported.

query_metrics

Overwrite ALL values (and period / locations / dimensions) of an existing target set with new data, preserving the prior state as a restorable version. Use this when the user wants to update or replace an existing target's numbers, or after a load_target TARGET_NAME_CONFLICT (pass its existing_target_set_id). target_set_id is a STRING (from list_targets or a conflict response). Resolve labels first and shape `values` exactly as in load_target — but this is single-metric: `metric_name` is the target's display name, and each value row's `values` dict is keyed by it, e.g. {"date": "...", "location_id": "...", "dimensions": {...}, "values": {"Sales budget": 7000}}. start_date / end_date follow load_target: the first day of the first bucket and the LAST day of the last one. Pass the business_id from your most recent get_analytics_context call.

replace_target_data

Change the active business for all subsequent tool calls. Returns the new business_id and business_name — use these for subsequent calls. After switching, re-call get_analytics_context: currency, timezone, week start and date_periods are business-specific.

switch_business