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wangyafu
by wangyafu

get_prompt

Access game rules and instructions for playing the 'Turtle Soup' puzzle game on the Haiguitang MCP Server using this tool. Start solving puzzles with clear guidance.

Instructions

获取海龟汤游戏的玩法

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'get_prompt' tool. It is registered using the @mcp.tool() decorator and simply returns the predefined 'prompt' string which contains the 海龟汤 (Turtle Soup) game rules and instructions.
    def get_prompt()->str:
        '''获取海龟汤游戏的玩法'''
        return prompt
    # 工具:选择谜题
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('获取海龟汤游戏的玩法') without describing how it behaves: e.g., whether it returns static instructions, dynamic content, requires authentication, has rate limits, or what format the output takes. For a tool with zero annotation coverage, this is a significant gap in transparency.

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, clear sentence ('获取海龟汤游戏的玩法') that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple, no-parameter tool and is front-loaded with the essential information. Every part of the description earns its place.

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?

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimal but adequate for basic understanding. However, it lacks context about what '海龟汤游戏' (turtle soup game) entails, how the gameplay information is structured, or any behavioral details. For a tool that might return complex instructions, more completeness would be helpful, but the low complexity keeps it from being a 1.

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 tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline expectation. No parameters means no additional burden for the description to compensate, justifying a score of 4.

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 the tool's purpose as '获取海龟汤游戏的玩法' (Get the gameplay of the turtle soup game), which is a specific verb ('获取' - get) and resource ('海龟汤游戏的玩法' - gameplay of turtle soup game). It distinguishes from sibling tools like 'get_puzzle' and 'list_puzzles_tool' by focusing on gameplay instructions rather than puzzles themselves. However, it doesn't explicitly contrast with siblings, keeping it at a 4 rather than a 5.

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

Usage Guidelines2/5

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 like 'get_puzzle' or 'list_puzzles_tool'. It doesn't specify whether this is for learning game rules before playing, troubleshooting, or other contexts. There's no mention of prerequisites, exclusions, or comparative use cases, leaving the agent to infer usage from the purpose alone.

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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