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revfanc

lanhu-ui

by revfanc
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: one provides region and implementation guidance, the other handles asset downloads. No overlap.

    Naming Consistency5/5

    Both tools follow a consistent snake_case pattern with 'lanhu_ui_' prefix and a noun indicating the resource (region, assets).

    Tool Count3/5

    With only 2 tools, the server feels very thin. While it may serve a narrow purpose, most similar servers offer more operations.

    Completeness2/5

    The server covers only two aspects of UI design interaction. Missing obvious tools like listing regions, getting design specs, or other common operations.

  • Average 2.7/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 6 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 full burden. The description only says it 'returns' data, but does not disclose side effects, authorization needs, or other behavioral traits like pagination or caching. For a tool with 13 parameters, this is insufficient.

    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 a single sentence with no fluff, but it is too terse. While concise, it sacrifices necessary detail for a complex tool.

    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 has 13 parameters, an output schema, and no annotations, the description should explain the region concept, pagination, and typical usage. Instead, it provides only a high-level hint, leaving significant gaps.

    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 baseline is 3. The description adds some context ('exact elements', 'implementation guidance') but does not clarify parameter semantics beyond what the schema already provides.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states 'Return exact elements plus flow/flex/grid-first implementation guidance,' which gives a general idea but does not clearly specify that this tool retrieves design region details from Lanhu. It lacks a precise verb-resource structure and does not differentiate from the sibling tool lanhu_ui_assets.

    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 usage guidance is provided. The description does not indicate when to use this tool versus the sibling lanhu_ui_assets, nor does it mention any context or alternatives. The agent must infer usage from the 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?

    With no annotations, the description carries full burden for behavioral traits. It only states 'Download the exact cuts', omitting critical info like whether it's read-only, pagination behavior, error conditions, or side effects. This is insufficient for an agent to understand the tool's behavior.

    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, no wasted words. However, it may be too terse, sacrificing completeness for brevity. Could include key behavioral or usage hints without bloating.

    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 5 parameters, one required, and an output schema, the description is too sparse. It doesn't explain return values, pagination mechanism (cursor, limit), optional variant usage, or force_refresh behavior. Output schema exists but is not leveraged in description.

    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 has 100% coverage with descriptions for all 5 parameters, so baseline is 3. The tool description itself adds no extra meaning beyond the schema, but that's acceptable given the schema completeness.

    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 uses specific verb 'Download' and resource 'exact cuts shown by Lanhu's cut-away mode', clearly indicating the tool's purpose. It distinguishes from sibling 'lanhu_ui_region' implicitly by focusing on downloading cuts, but does not explicitly differentiate.

    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, no context about prerequisites or non-use cases. The description does not reference sibling tools or provide any selection criteria.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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