get_prompt
Retrieve gameplay instructions for Situation Puzzle games where players solve mysterious scenarios by asking yes/no questions to uncover hidden stories.
Instructions
获取海龟汤游戏的玩法
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Retrieve gameplay instructions for Situation Puzzle games where players solve mysterious scenarios by asking yes/no questions to uncover hidden stories.
获取海龟汤游戏的玩法
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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. The description only states what the tool does ('获取海龟汤游戏的玩法'), but does not disclose any behavioral traits such as whether it requires authentication, has rate limits, returns structured or unstructured data, or if it's idempotent. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence in Chinese ('获取海龟汤游戏的玩法') that directly states the tool's purpose without any unnecessary words or fluff. It is appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It explains what the tool does but lacks details on behavioral aspects and usage context. For a tool that likely returns explanatory text about gameplay, the absence of an output schema means the description should ideally hint at the return format, but it does not. This results in a baseline score that meets minimum viability but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). With no parameters, the description does not need to add semantic details beyond the schema. The baseline score for 0 parameters is 4, as there is no parameter information to compensate for or enhance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description '获取海龟汤游戏的玩法' (Get the gameplay of the turtle soup game) clearly states the tool's purpose with a specific verb ('获取' - get) and resource ('海龟汤游戏的玩法' - gameplay of turtle soup game). It distinguishes this from sibling tools like 'get_puzzle' (which likely retrieves a specific puzzle) and 'list_puzzles_tool' (which likely lists multiple puzzles), as this tool focuses on explaining how to play the game rather than retrieving puzzle content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context for usage, or differentiate from sibling tools beyond what can be inferred from their names. Users must deduce usage based on tool names alone, which is insufficient for clear decision-making.
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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