clariBI.com

get_forecast_run

Read-onlyIdempotent

Fetch one run of a forecast by ID, or pass run_id="latest" for the most recent run. Returns the full forecast envelope: target series + 30-day projection, correlated drivers (with lag and bootstrap stability), anomalies, structural changes, and credit accounting.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`run_id`	Yes	UUID of the run. Pass 'latest' to fetch the most recent run for the forecast.
`forecast_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description
`id`	Yes
`result`	No	Forecast envelope. Contains keys: target, correlations, anomalies, changepoints, meta, and (when narration ran) narration. `target` carries lower/upper plus calibrated lower_50/upper_50, lower_80/upper_80, lower_95/upper_95 bands, the winning method, exogenous_driver when a leading-indicator was used, ensemble_components when the ensemble method won, transform applied, and non_negative flag. See docs/MCP_SERVER.md for the shape.
`status`	Yes
`trigger`	No
`narration`	No	Peer of result.narration for convenience. Null when the run pre-dates narration OR was gated out (insufficient credits, org disabled, etc.).
`created_at`	No
`duration_ms`	No
`forecast_id`	Yes
`completed_at`	No
`error_message`	No
`credits_consumed`	No
`credits_refunded`	No

TDQS

A4.3/5.0

Behavior4/5

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

The annotations already declare readOnlyHint and idempotentHint, and the description adds the special behavior of run_id='latest' and the detailed contents of the returned envelope (drivers, anomalies, etc.). No contradiction with 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?

Two concise sentences, front-loaded with the action, and every clause adds information (special case, return contents). No redundancy.

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

Completeness5/5

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

The description, combined with the input schema and annotations, provides sufficient information for correct invocation: required parameters, the 'latest' keyword, and the expected results. No gaps identified.

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

Parameters3/5

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

The schema documents run_id with a description including the 'latest' special value, but forecast_id lacks a description. The tool description reinforces the run_id behavior and mentions fetching by ID, but adds no specific meaning for forecast_id beyond the schema's type/format. With 50% schema coverage, the description partially compensates.

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 uses the specific verb 'Fetch' and identifies the resource as 'one run of a forecast,' with a clear special-case for run_id='latest'. It contrasts with sibling tools like list_forecasts and run_forecast, making its purpose distinct.

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 for when to use the tool (to retrieve a specific forecast run, including the latest) but does not explicitly mention alternatives or when not to use it. The context of returning a full forecast envelope implies its use case.

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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