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YBMecho

Uiverse MCP Tool

by YBMecho
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Python
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: one lists supported frameworks, and the other extracts code from a given link. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern in snake_case: 'parse_and_extract' and 'list_supported_frameworks'. This is consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels minimal but not absurdly under-scoped. The tools cover the core extraction workflow, though the count is on the low end of what would be expected.

    Completeness4/5

    The tool set covers the essential lifecycle: knowing which frameworks are supported and then extracting code from a Uiverse link. A minor gap is the lack of any search or discovery tool for finding components, but the stated purpose is extraction, which is well covered.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

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  • 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

  • Behavior3/5

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

    With no annotations, the description must disclose behavior itself. It reveals the URL-prefix condition and that a framework-based MCP implementation is selected to extract code. It does not describe the actual extraction result, return format, or side effects, leaving the behavioral profile incomplete.

    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 well-organized with a rule section, example, and bulleted AI-calling instructions. It is reasonably concise, though the conditional bullet points could be condensed without losing information.

    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?

    For a one-parameter tool with an existing output schema, the description covers the input format, the embedded conditional, and the case where the tool should not be used. It omits handling for non-matching URL prefixes and unsupported frameworks, but the main invocation path is clearly specified, so the description is largely complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema provides only a 'query' string with no description (0% coverage). The description compensates fully by specifying the required format 'framework + space + link', including an example and the conditional constraint that the link must begin with the uiverse.io prefix. This adds substantial meaning beyond the schema.

    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 states that the tool extracts code from a uiverse.io link using a framework name, with the input format 'framework + space + link'. It is more specific than the generic name and implies a different role than the sibling list_supported_frameworks. However, the core action is embedded in conditional logic, making it slightly less direct.

    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?

    It explicitly says to use the MCP when the link starts with 'https://uiverse.io/' and has content after the prefix, and to NOT use MCP when there is no content. This gives clear when-to-use and a notable when-not-to-use, but it does not cover links that don't start with the prefix or unsupported frameworks.

    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?

    With no annotations provided, the description carries the full burden. It explicitly states the read-only nature ('list') and the Markdown output format, which are useful behavioral traits. However, it does not discuss any potential variability (e.g., sorting, emptiness) or constraints, but for a simple list tool this is acceptable.

    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, front-loaded sentence that directly conveys the action and result. Every word earns its place with no redundancy.

    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?

    Given the tool's trivial complexity (no parameters) and the presence of an output schema, the description sufficiently covers what is returned (framework names in Markdown). It is complete for an AI agent to understand and invoke the tool.

    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 tool has zero parameters, so the baseline is 4. There is nothing for the description to explain beyond the schema, and no parameter ambiguity exists.

    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 uses the specific verb '列出' (list) and a clear resource '当前支持的框架名称' (currently supported framework names), with the output format (Markdown) noted. It clearly distinguishes from the sibling tool parse_and_extract.

    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 is provided on when to use this tool versus alternatives, nor any context about typical use cases or prerequisites. It merely states what the tool does without explaining when to invoke it.

    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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