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veroq_forecast

Generate predictive forecasts for any topic using intelligence trends, momentum, and historical patterns. Get outlook, confidence, time horizon, key drivers, risks, and probability-weighted scenarios.

Instructions

Generate a forward-looking forecast for a topic based on intelligence trends, momentum, and historical patterns.

WHEN TO USE: When you need predictive analysis — likely outcomes, scenarios, and risk factors for a topic. RETURNS: Outlook, confidence, time horizon, key drivers, risks, probability-weighted scenarios, and supporting briefs. COST: 2 credits. EXAMPLE: { "topic": "US inflation trajectory", "depth": "deep" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesTopic to forecast future developments for
depthNoAnalysis depth
Behavior4/5

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

No annotations provided, so description bears full transparency burden. It lists return fields (Outlook, confidence, etc.) and cost (2 credits), but does not explicitly state if the operation is read-only or has side effects. Given the tool's nature, it's likely safe, but more explicit behavioral detail would improve score.

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 very concise, using headings (WHEN TO USE, RETURNS, COST, EXAMPLE) for clear structure. Every sentence adds value, and the main verb is front-loaded.

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?

For a tool with only 2 parameters and no output schema, the description covers all essential aspects: purpose, usage guidance, return format, cost, and an example. No critical information is missing.

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?

Schema description coverage is 100%, so baseline is 3. The description provides an example usage for the 'depth' parameter but adds no significant meaning beyond what the schema already states. The param descriptions are already clear.

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's function: 'Generate a forward-looking forecast for a topic based on intelligence trends, momentum, and historical patterns.' It distinguishes itself from siblings like 'veroq_brief' and 'veroq_intelligence' by emphasizing predictive analysis.

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?

Includes a dedicated 'WHEN TO USE' section: 'When you need predictive analysis — likely outcomes, scenarios, and risk factors for a topic.' This provides clear context, but does not mention alternatives or when not to use, which would elevate it to a 5.

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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