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luminarylane

Design Style MCP Server

by luminarylane
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation5/5

    Both tools have clearly distinct purposes: get_style retrieves details for a known style, recommend_style suggests a style based on context. No overlap.

    Naming Consistency5/5

    Both tool names follow the verb_noun pattern (get_style, recommend_style) consistently, making them predictable.

    Tool Count4/5

    With only 2 tools, the server is minimal but still covers the core functionality for its narrow domain. A list tool would be useful but not mandatory.

    Completeness3/5

    Covers retrieval and recommendation, but lacks a tool to list all available style slugs programmatically, which is a notable gap since get_style requires a valid slug.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 commits in the last 12 weeks
    • Last stable release on
    • 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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    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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden. It explicitly states 'No AI inference — uses deterministic scoring against style characteristics', which clarifies the computational behavior and lack of AI stochasticity. It does not detail auth needs or rate limits but is adequate for a read-only recommendation tool.

    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?

    Two concise sentences: first states the purpose, second adds critical behavioral info (deterministic, returns reasoning and alternatives). No wasted words, front-loaded with core action.

    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 4 parameters (2 required), no output schema, and no annotations, the description adequately explains inputs and output (top match with reasoning and alternatives). It could elaborate on the return structure or examples, but is sufficiently complete for an agent to understand the tool's function.

    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%, and the description does not add meaning beyond what the schema already provides. The examples in the description are similar to the schema's description, so no additional value is added. Baseline of 3 is appropriate.

    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 it 'recommends a design style' based on brand, objective, and demographic. It also distinguishes itself from the sibling tool 'get_style' by specifying it returns a top match with reasoning and alternatives, and clarifies it uses deterministic scoring versus AI inference.

    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?

    The description implies usage for recommending a style based on inputs, but does not explicitly state when to use it versus alternatives like 'get_style'. There is no guidance on when not to use it or specific prerequisites.

    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 are provided, so the description carries full burden. It describes the tool as read-only (retrieve) and lists the return content. No side effects or auth requirements are mentioned, but for a simple retrieval, this is transparent enough. No contradictions.

    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 two sentences, each earning its place: first states purpose and return content, second lists valid slugs. No unnecessary words, front-loaded with essential information.

    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 single-parameter retrieval tool with no output schema, the description is complete: it explains what the tool does, what it returns (including specific sections), and the valid inputs. No gaps remain for the agent to infer.

    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?

    Schema coverage is 100% with a descriptive parameter. The description adds the list of 30 valid slugs beyond the schema's brief example, providing concrete enumeration that helps the agent select valid inputs.

    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 retrieves structured design style tokens for AI content generation, lists the specific sections returned (name, description, color palette, typography, mood, visual directives, negative prompt), and distinguishes from the sibling 'recommend_style' by focusing on retrieving known style tokens rather than recommending.

    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?

    The description implies use when a specific style's full definition is needed, and lists 30 valid slugs. While no explicit when-not-to-use or alternative is given, the context of sibling 'recommend_style' suggests that tool is for recommendations. Clear context but lacks exclusions.

    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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