Haiguitang MCP Server
Server Quality Checklist
Latest release: v0.1.2
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: get_prompt explains game rules, get_puzzle retrieves specific puzzle content, and list_puzzles_tool shows available puzzles. The descriptions clearly differentiate between instructional, retrieval, and listing functions.
Naming Consistency4/5The naming follows a consistent verb_noun pattern (get_prompt, get_puzzle, list_puzzles_tool) with clear action-object relationships. The minor deviation is 'list_puzzles_tool' having '_tool' suffix while others don't, but the core pattern remains readable and predictable.
Tool Count3/5With only 3 tools, the count feels minimal for a game server. While it covers basic operations (list, retrieve, instructions), it lacks puzzle creation, management, or interactive solving tools that would be expected for a complete game experience.
Completeness2/5The toolset has significant gaps for a game server. It provides only read operations (get, list) with no puzzle creation, updating, deletion, or gameplay interaction tools. Users can view puzzles but cannot submit answers, track progress, or manage content - making this a passive viewing experience rather than an interactive game.
Average 2.9/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
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. It states the tool retrieves puzzle content, implying a read-only operation, but doesn't disclose any behavioral traits such as error handling, authentication needs, rate limits, or what '完整内容' (complete content) entails. This leaves significant gaps for an AI agent to understand how to invoke it correctly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the main purpose, but it includes unnecessary formatting like 'Args:' and 'Returns:' sections that don't add value beyond the schema. The sentences are efficient, but the structure could be improved by integrating the parameter info more seamlessly or omitting redundant parts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (1 parameter, no annotations, no output schema), the description is incomplete. It doesn't explain what '完整内容' (complete content) includes, how errors are handled, or what '选择结果信息' (selection result information) means in the returns. Without annotations or output schema, more detail is needed for the agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal meaning beyond the input schema. It documents the parameter 'puzzle_title' as '海龟汤的标题' (turtle soup title), which provides some context, but with 0% schema description coverage, the schema only specifies the parameter name and type. The description doesn't compensate by explaining format, constraints, or examples, leaving the parameter poorly defined.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '获取一个谜题的完整内容' (get the complete content of a puzzle). It specifies the verb '获取' (get) and resource '谜题' (puzzle), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'list_puzzles_tool' or 'get_prompt', which could be used for similar purposes, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 doesn't mention sibling tools like 'list_puzzles_tool' (which might list puzzles) or 'get_prompt' (which might retrieve prompts), nor does it specify prerequisites or exclusions. Usage is implied by the parameter 'puzzle_title', but no explicit context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
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. While it states the tool returns a puzzle list, it doesn't describe format, pagination, error conditions, or any side effects. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief but includes unnecessary structural elements. The 'Returns:' section adds minimal value since it just restates the obvious (returns a puzzle list). The two-sentence structure could be condensed to a single, more direct statement without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a list operation. It doesn't specify what 'available puzzles' means, the format of the returned list, or any constraints. For a tool that presumably returns data, more context about the response structure would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description appropriately doesn't add parameter information, maintaining a baseline of 4 for tools with no parameters where the schema handles documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as '列出所有可用的谜题' (list all available puzzles), which is a specific verb+resource combination. However, it doesn't distinguish this tool from its sibling 'get_puzzle' (which likely retrieves a specific puzzle), so it doesn't fully differentiate from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. There's no mention of when to use 'list_puzzles_tool' versus 'get_puzzle' or 'get_prompt', nor any context about prerequisites or exclusions. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
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.
Conciseness5/5Is 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.
Completeness3/5Given 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.
Parameters4/5Does 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.
Purpose4/5Does 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.
Usage Guidelines2/5Does 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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