lanhu-design-mcp

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Server Quality Checklist

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Latest release: v0.2.0
Disambiguation5/5
Each tool has a clearly distinct purpose: authentication (login, status, logout) and design operations (list, analyze, get assets, export context). There is no overlap, and descriptions clarify any potential confusion between get_design_assets and export_ui_context.
Naming Consistency5/5
All tools follow a consistent 'lanhu_' prefix with a verb_noun pattern (e.g., lanhu_auth_login, lanhu_get_designs, lanhu_export_ui_context). The only deviation is lanhu_health_check, which is still a common compound noun and does not break consistency.
Tool Count5/5
With 8 tools, the server is well-scoped for its domain of authentication and design retrieval/analysis. Each tool serves a necessary function without redundancy, and the count is within the ideal 3-15 range.
Completeness4/5
The tool surface covers authentication, health, listing, analysis, and asset retrieval. A minor gap is the lack of a dedicated 'get_design' tool for basic design details without analysis, but the list and analysis tools effectively cover this need.
Average 3.3/5 across 8 of 8 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 52 commits in the last 12 weeks
- Last stable release on July 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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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?
With no annotations, the description must fully disclose behavior. It only states no credential info is included, but doesn't mention if authentication is required, what status values exist, or side effects. Very sparse.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, with one short sentence. While there is no fluff, it lacks structure and does not provide parameter details or usage notes.
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 optional parameter and the presence of an output schema, the description is too minimal. It does not explain return values, possible errors, or when to invoke this tool. Incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not mention the optional session_id parameter at all. The agent gets no help understanding the parameter's meaning or usage.
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 reports hosted authentication status without credentials, distinguishing it from auth login/logout tools. However, it could be more specific about what 'status' entails.
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?
No explicit guidance on when to use this tool versus alternatives. Siblings include login, logout, and health check, but no comparison is provided. The agent must infer usage.
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 provided, so description bears full burden. It only states what the tool returns, not any behavioral traits such as authentication requirements, rate limits, or side effects. It does not clarify whether the tool is read-only or modifies state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (one sentence in Chinese). It is concise but not structured for quick scanning. It does not front-load the core action or highlight key parameters. Could be more efficient for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the return structure is documented separately. The description adequately states the high-level purpose but lacks details such as how fine-grained cutouts are specified or related to parameters. It is minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions in English (e.g., 'Output platform', 'Design exact name...'). The description adds no extra meaning beyond what the schema already provides. Baseline of 3 is appropriate.
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?
Description states it returns complete design drawings and fine-grained downloadable resources (切图资源). Verb '返回' and resource '设计图与切图资源' are specific. It is not a tautology, but does not explicitly differentiate from sibling tool 'lanhu_get_designs' which likely lists designs.
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?
No guidance on when to use this tool vs alternatives. No mention of prerequisites, contexts, or exclusions. The description does not help an agent decide between this and sibling tools like 'lanhu_get_designs' or 'lanhu_analyze_design'.
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 provided, so description must carry behavioral info. It only states retrieval; does not mention if it modifies data, requires auth, or has side effects. Read-only nature is implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise and front-loaded. However, could include more useful information without much added length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Low complexity with 1 param and output schema present. Description covers core function but lacks context about authentication requirements and relationship to sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter 'url' is described in the schema (100% coverage). The tool description adds no extra meaning beyond the schema description, which is already clear. Baseline of 3 is appropriate.
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?
Description clearly states action (get list) and resource (designs for project). However, it does not differentiate from sibling tools like lanhu_analyze_design or lanhu_get_design_assets, which might overlap.
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?
No guidance on when to use this tool versus alternatives, nor prerequisites (e.g., authentication). The description is silent on context.
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?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states what the tool returns but fails to describe potential side effects, rate limits, authentication requirements, or performance implications. The read-only nature is implied but not confirmed.
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 sentence that is clear and contains no extraneous information. Every word contributes to the purpose, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to detail return values. However, it does not fully explain the concept of 'Agent UI 还原上下文' or how the tool combines assets and analysis, which may leave an AI agent needing to infer details from sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add any extra information about parameters beyond what is already in the schema, but it also does not detract.
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 returns a complete Agent UI restoration context including assets and analysis, using a specific verb ('返回') and resource. However, it does not explicitly distinguish itself from sibling tools like 'lanhu_get_designs' or 'lanhu_analyze_design', though the combination of assets and analysis hints at its unique role.
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 (e.g., 'lanhu_get_design_assets', 'lanhu_analyze_design'). No when/when-not advice is given, leaving the agent to infer usage from the tool name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description partially fills the gap by noting the login is 'interactive' (requiring user action via a Chrome Profile), which hints at non-automated behavior. However, it does not disclose critical details: whether the process is blocking, how long it takes, whether it caches sessions, or what state changes occur on the system. The interactive nature is useful but incomplete.
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 sentence with no wasted words. It front-loads the action and resource, and every word ('打开专属 Chrome Profile 进行交互式 Lanhu 登录') is meaningful. No fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (login, interactive) and the existence of an output schema (not shown), the description provides minimum viable context. It explains what the tool does but omits details like what 'interactive' entails (e.g., user must confirm in browser), side effects on existing sessions, or post-login behavior. The lack of prerequisites or expected outcomes reduces completeness.
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 has zero parameters and 100% coverage, so the description need not explain parameters. It adds no redundant information. According to guidelines, 0 parameters earns a baseline of 4, which is appropriate as the description is harmlessly silent on parameters.
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: opening a dedicated Chrome Profile for interactive Lanhu login. It uses a specific verb ('打开') and resource ('Chrome Profile', 'Lanhu登录'), and distinguishes itself from sibling tools like lanhu_auth_status and lanhu_auth_logout, which handle session state.
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. It does not mention prerequisites (e.g., whether user is already logged in), expected usage order, or when to prefer lanhu_auth_login over other auth tools. The agent is left to infer that login must precede other operations, but this is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
无注解信息，描述承担全部行为透明度负担。描述了分析并返回UI结构，但未说明是否只读、权限要求、错误处理或行为副作用，深度不足。
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
描述为一句中文，简洁且前部聚焦，无冗余内容。但缺少结构化分点或使用提示，略欠精细。
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
工具具有输出模式但描述未提及返回值或使用场景；虽无需解释输出格式，但平台调整含义及参数配合逻辑可更充分。整体尚可但有提升空间。
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
输入模式已100%描述参数（url、target_platform、design_name_or_index），描述未在模式之外增加额外语义信息，符合基线3分。
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?
描述明确说明工具用于分析指定设计稿并返回调整后的UI结构，动词和资源清晰。与同类工具如lanhu_get_designs（列举设计稿）和lanhu_get_design_assets（获取素材）有明显区分，用途一目了然。
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?
描述未明确说明何时使用此工具而非其他工具，也未提供排除条件。只能通过工具名称和上下文推断，缺乏直接的when-to-use指导。
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses key behaviors (no network access, no cookie exposure) but does not detail return structure or potential side effects. Some transparency but not comprehensive.
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?
Single sentence, front-loaded with the main action. Every word counts. No redundancy.
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 zero parameters and output schema exists, the description adequately covers purpose and key behavioral constraints. However, it could mention the output format or typical use cases.
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?
No parameters exist; schema is empty. The description adds value by explaining the purpose and behavioral constraints beyond the empty schema. With 100% trivial coverage, baseline is 3, but the description compensates.
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 clearly states the tool returns local configuration status and explicitly mentions it does not access network or expose cookies. This distinguishes it from sibling tools like auth or design operations.
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?
No guidance on when to use this tool versus alternatives. The description implies use for checking local config without network, but lacks explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It clearly discloses the destructive behavior (logout and delete managed profile) and the confirmation requirement, which is adequate for a simple authentication action.
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?
Single sentence front-loads the action and requirement, with no extraneous words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and output schema present, the description is sufficient: it explains the primary purpose and parameter requirement. No further behavioral details are needed given the tool's straightforward nature.
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?
Input schema has one boolean parameter confirm with default false and no description. Description compensates by stating '需要 confirm=true 确认', clarifying that the parameter must be true to execute the action, adding meaning beyond the schema.
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?
Description states '登出并删除托管 Profile' (logout and delete managed profile), specifying a clear verb+resource combination. It distinguishes from siblings like lanhu_auth_login and lanhu_auth_status by indicating a destructive action.
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?
Description implies use when logout and profile deletion are needed, and requires confirm=true. However, it lacks explicit context on when not to use or alternatives, though the action is self-evident.
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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