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themesberg

flowbite-mcp

by themesberg
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JavaScript

Server Quality Checklist

83%
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  • Latest release: v1.1.5

  • Disambiguation5/5

    The two tools have completely different purposes: one converts Figma designs to code, the other generates a theme from a brand color. There is no overlap or ambiguity.

    Naming Consistency4/5

    Both tools use snake_case and follow a verb-object pattern, though 'convert-figma-to-code' is more detailed than 'generate-theme'. The inconsistency is minor.

    Tool Count3/5

    With only two tools, the surface feels thin for a design-to-code and theme generation server. However, the scope is narrow enough that the count is borderline acceptable.

    Completeness4/5

    The tools cover the core workflows of converting a design and generating a theme. Missing features like theme validation or export are minor gaps for the stated purpose.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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

  • Behavior3/5

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

    No annotations are provided, so the description must convey behavioral traits. It states that the tool generates a CSS file and modifies theme variables intelligently, but lacks details on side effects (e.g., file overwriting, permissions, reversibility). It does not contradict annotations as none exist.

    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?

    The description is relatively concise with two sentences that front-load the purpose. The second sentence expands on the AI behavior and scope without unnecessary words. It could be slightly more concise, but overall it is 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?

    The description covers the tool's purpose and inputs adequately but lacks detail on the output format (e.g., whether the CSS content is returned directly or saved to a file). Given no output schema and no annotations, additional context about the return value or side effects would improve 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 provides 100% coverage with descriptions. The description adds value by explaining that the AI analyzes instructions holistically to customize all theme variables, and that brandColor is used as a base for all color variations. This goes beyond the schema's parameter-level descriptions.

    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 that the tool generates a custom Flowbite theme CSS file based on brand color and instructions. It specifies the output (CSS file) and the key inputs. The sibling tool 'convert-figma-to-code' is unrelated, so differentiation is clear.

    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 context by describing the AI's role in analyzing instructions and customizing all theme variables. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention when not to use it or any 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 the full burden. It discloses the authentication requirement (FIGMA_ACCESS_TOKEN) and implies a read-only operation by stating 'fetches' and 'converts'. It does not mention rate limits or mutability, but the behavioral transparency is adequate for the tool's simplicity.

    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 long, with the first sentence stating purpose and the second stating a prerequisite. No unnecessary words. Efficiently conveys essential information.

    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?

    The description covers the basic purpose and prerequisite, but lacks details about the output format (e.g., what type of code block). Since there is no output schema, the description should provide more completeness about the return value. It is somewhat incomplete.

    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 schema already documents the single parameter fully. The description adds no additional meaning beyond what the schema provides, earning a baseline score of 3.

    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 verb 'fetches' and 'converts', and the resource 'Figma node and its rendered image'. It distinguishes the tool from the sibling 'generate-theme' by its specific action. Purpose is unambiguous.

    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 mentions the prerequisite FIGMA_ACCESS_TOKEN, providing essential context. However, it does not specify when to use this tool over alternatives or when not to use it. No explicit usage guidelines beyond the token requirement.

    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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