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run_custom_forecast

Run a bespoke Kronos forecast by specifying symbol, timeframe, horizon, lookback, and Monte-Carlo sample paths. Get custom AI-powered predictions for Bitcoin price or fees.

Instructions

Run a bespoke Kronos forecast (paid POST).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
horizonYesSteps to forecast ahead.
lookbackYesHistorical candles of context.
timeframeYes
sample_countYesMonte-Carlo sample paths.
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does clearly state that the operation is 'paid POST', which is crucial context. However, it omits other important behaviors such as cost amount, authentication requirements, response shape, or whether the operation is asynchronous.

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 a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core purpose and key cost/method alert, making every word count.

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

Completeness2/5

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

With 5 required parameters, no output schema, and no annotations, the description is under-specified. It alerts the agent to the paid POST nature but does not explain what the forecast returns, how to interpret the output, or any prerequisites or side effects.

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 schema covers 60% of parameters with descriptions (horizon, lookback, sample_count), and the remaining two (symbol, timeframe) are self-explanatory. However, the description adds no parameter-level meaning at all, leaving the agent to infer the semantics solely from names and the partial schema.

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

Purpose4/5

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

The description uses a specific verb ('Run') and identifies the resource as a 'bespoke Kronos forecast', with the critical context that it's a 'paid POST'. This distinguishes it from the read-only get_* siblings, though 'Kronos' is not defined and the phrase 'custom forecast' already appears in the name.

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

Usage Guidelines3/5

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

The word 'bespoke' implies a custom, non-standard use case and contrasts with the sibling get_* forecast tools, but the description provides no explicit when-to-use or when-not-to-use guidance, nor does it name any alternative tools.

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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