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risk_kelly

Read-onlyIdempotent

Calculate optimal bet size using the Kelly Criterion. Choose discrete (win/loss) or continuous (returns series) mode.

Instructions

Kelly Criterion: discrete (win/loss) or continuous (returns series) mode.

Use when determining optimal bet/position sizing using the Kelly Criterion. Provide win probability and win/loss ratio. Returns: full Kelly fraction, half-Kelly, quarter-Kelly, and expected growth rate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoCalculation mode: discrete (win/loss) or continuous (return series)discrete
avg_winNoAverage win amount, required for discrete mode
returnsNoArray of historical returns, required for continuous mode
avg_lossNoAverage loss amount (positive number), required for discrete mode
win_rateNoProbability of winning (0-1), required for discrete mode
Behavior3/5

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds no behavioral detail beyond the annotations, but does not contradict them.

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 extremely concise (4 sentences), front-loading the key information (Kelly Criterion, discrete/continuous modes) without any wasted words.

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?

The description outlines the return values (full Kelly, half-Kelly, etc.) and usage context. Since there is no output schema, the description adequately covers what to expect. Missing details like handling of edge cases or validation are not critical for this tool.

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

Parameters4/5

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

The input schema covers all 5 parameters with descriptions (100% coverage). The description adds context by stating 'Provide win probability and win/loss ratio' and mentions the two modes, reinforcing the 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 clearly states that the tool calculates the Kelly Criterion for optimal bet sizing in discrete or continuous modes. However, it does not explicitly differentiate from sibling tools like risk_position-size, which may also involve position sizing.

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 description says 'Use when determining optimal bet/position sizing using the Kelly Criterion,' giving clear context. It does not provide when-not-to-use advice or mention alternatives, such as risk_position-size.

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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