clariBI.com

create_forecast

Bind a forecast to a metric in your workspace. Stores the configuration, sets up the schedule, and resolves the source binding immediately so a bad source_id or metric_path errors out before any credits are spent. Run the forecast with run_forecast.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`source_id`	Yes	UUID of the report, dashboard, goal, or data source.
`transform`	No	Series transform applied before fitting. Log helps revenue/traffic series with growing variance. Auto detects when log is beneficial; none forces raw scale.	auto
`aggregation`	No	How multiple raw points falling into the same bucket are combined. Auto picks sum for additive metrics, mean otherwise. Use Mean for rates (CTR, conversion, latency).	auto
`description`	No
`granularity`	No	Bucket size for the time series. Hourly requires an hourly data source; the engine refuses to fabricate hourly buckets from daily data.	daily
`metric_path`	Yes	Dot-path to the numeric column. For data_source: the metric key (e.g. "revenue"). For goal: "value" or "percentage". For report: the path inside the insights JSON. For dashboard: "widget_id:metric_name".
`source_type`	Yes	Which clariBI artifact the metric lives on. Most callers should start with data_source for raw connector metrics or goal to forecast a tracked KPI.
`horizon_days`	No	How many BUCKETS ahead to project. The per-granularity cap applies: hourly 168, daily 90, weekly 52, monthly 24, quarterly 8, annual 5. The field name is "_days" for backwards compatibility — it counts buckets of the chosen granularity.
`non_negative`	No	When true, point + lower band clamped at zero. Omit to let the engine heuristic decide (via non_negative_auto=true).
`method_override`	No	Force a specific forecasting method instead of auto-selecting via walk-forward CV. Valid names: naive, seasonal_naive, moving_average, drift, linear_trend, ar_p, holt_winters, holt_winters_multiplicative, holt_winters_damped, theta, gradient_boost, ensemble_top3. Empty = auto.
`include_anomalies`	No
`narration_enabled`	No	When true, every completed run gets an AI-generated narrative attached (summary, highlights, risks, recommendations, methodology). Costs 1-2 extra AI credits per run.
`non_negative_auto`	No	When true (default), the backend heuristic owns non_negative and re-evaluates per run based on the metric path. Set false alongside non_negative to lock the choice.
`schedule_frequency`	No	How often the forecast re-runs. Manual schedules only run when called explicitly via run_forecast.	monthly
`include_changepoints`	No
`include_correlations`	No
`schedule_day_of_week`	No
`schedule_day_of_month`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes
`name`	Yes
`web_url`	Yes
`granularity`	No
`next_run_at`	No
`horizon_days`	Yes
`metric_label`	No
`schedule_frequency`	No

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite sparse annotations, the description discloses key behaviors: it stores configuration, sets up scheduling, and performs immediate source binding validation so errors surface before credits are spent. It also implies that running occurs separately via run_forecast, adding context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the primary purpose. Three sentences cover purpose, key behaviors, and a pointer to the related tool. No filler or redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (19 parameters, output schema exists), the description captures the essential workflow and validation behavior. It lacks a brief orientation on key parameters, but the output schema and parameter descriptions partially compensate, and the description clearly differentiates from run_forecast.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides no parameter-specific guidance, relying solely on the input schema. With schema coverage at 63%, several parameters (e.g., schedule_day_of_week, include_anomalies) lack schema descriptions, and the tool description does not compensate by summarizing or prioritizing these fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool binds a forecast to a metric, stores configuration, sets up the schedule, and resolves source binding. It distinguishes itself from siblings by explicitly naming run_forecast as the tool to use for execution, making its role as a setup tool unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that this tool is for configuration and setup, and explicitly directs the agent to run_forecast to execute. However, it does not offer explicit when-not-to-use scenarios or prerequisites beyond the implied need for a valid source_id and metric_path.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0

Disambiguation4/5

Tools are generally distinct by resource and action, but a few status polling tools (check_integration_status, get_analysis_status) could be confused without careful reading; descriptions clarify the difference.

Naming Consistency4/5

Most tools use a consistent verb_noun snake_case pattern, but there is minor variation (e.g., 'check' vs 'get' for status, and some compound nouns like 'request_oauth_integration_url').

Tool Count4/5

26 tools is slightly above the typical range but appropriate for a comprehensive BI platform covering data ingestion, analysis, forecasting, reports, dashboards, and account management; each tool has a clear purpose.

Completeness2/5

The tool surface is heavily read-oriented, lacking update and delete operations for most resources (data sources, dashboards, reports, forecasts). This leaves significant lifecycle management gaps for an agent.

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