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razz_play_limbo

Set a target multiplier to test probability in a provably fair game, with win chance calculated as 98% divided by your target. Higher targets increase potential payout but reduce odds, featuring optional SOL wagering.

Instructions

Play Limbo - set a target multiplier and hope the generated multiplier meets or exceeds it. Higher targets = bigger payouts but lower odds. Win chance = 98% / target. 2% house edge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
target_multiplierYesTarget multiplier (1.01-1000). Higher = bigger payout but lower chance. Win chance = 98% / target.
wagerAmountNoAmount to wager in SOL (0 or omit for free play, max 0.1)
Behavior4/5

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

With no annotations provided, the description carries full burden and successfully discloses critical behavioral traits: the win probability formula (98% / target), the house edge (2%), and the payout structure. This gives the agent full transparency on the gambling mechanics and financial risks.

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 front-loaded with the action ('Play Limbo') and follows with three efficient sentences covering mechanics, odds, and house edge. Zero wasted words; every clause provides essential information for decision-making.

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 (simple 2-parameter game), lack of annotations, and absence of output schema, the description is remarkably complete. It explains game rules, win conditions, probability mathematics, and wagering context, covering everything necessary for an agent to invoke the tool correctly.

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?

Schema coverage is 100%, establishing a baseline of 3. The description adds significant semantic value by explaining the relationship between target_multiplier values and outcomes ('Higher targets = bigger payouts but lower odds'), providing context beyond the raw numeric range of 1.01-1000.

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 explicitly states the verb and resource ('Play Limbo') and explains the core mechanic (setting a target multiplier). It clearly distinguishes from siblings like razz_play_dice or razz_play_crash by specifying the unique Limbo gameplay loop.

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?

While it does not explicitly name alternative game tools, it provides clear context for when to use this tool (when you want to set a target multiplier and gamble on exceeding it) and explains the risk/reward tradeoff ('Higher targets = bigger payouts but lower odds'), which implicitly guides selection.

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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