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Server Quality Checklist

67%
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  • Latest release: v1.0.4

  • Disambiguation5/5

    With only one tool in the set, there is no possibility of confusion or overlap with other tools within this server.

    Naming Consistency4/5

    The single tool uses clear snake_case with a descriptive verb suffix, but with only one data point, a broader naming pattern cannot be established.

    Tool Count2/5

    A single analysis tool is too few for the apparent scope implied by the server name 'mcp-sketch', suggesting a severely limited integration surface.

    Completeness2/5

    The server only supports analyzing HTML exports, lacking any functionality for creating, editing, or exporting native Sketch files, representing significant gaps for design workflow automation.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 168 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.

  • Add a glama.json file to provide metadata about your server.

  • This server has been verified by its author.

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 provided, the description carries the full burden of behavioral disclosure but fails to specify critical traits: it doesn't clarify that the tool likely extracts/reads zip files (implied by 'sketch html zip file path'), doesn't explain what the saveResult parameter actually saves or where, and doesn't disclose whether this is a read-only operation or has side effects.

    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 a single, efficient sentence with no redundant words. It is appropriately front-loaded with the verb 'Analyze'. However, the density means it sacrifices clarity on parameter relationships and behavioral details that would require additional sentences.

    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?

    For an 8-parameter tool handling file processing with multiple optional filters (page, artboard, rect) and persistence options (saveResult), the description is inadequate. With no output schema provided, the description should explain the return format and structure, but it omits this entirely, leaving the agent uncertain about what data structure it will receive.

    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?

    The input schema has 100% description coverage, establishing a baseline of 3. The description adds marginal semantic value by referencing 'pages or drawing boards' which contextualizes the page_id and artboard_id parameters, but doesn't explain the rect parameter's purpose (cropping vs selection) or the assets_path behavior.

    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 identifies the action ('Analyze') and resource ('sketch html files'), and mentions the return value ('design structure'). However, 'design structure' remains somewhat vague—it doesn't specify whether this returns layer hierarchies, CSS properties, or component trees, which would help the agent understand the utility of the output.

    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 mentions 'pages or drawing boards' which implicitly maps to the page_id/artboard_id parameters, but provides no explicit guidance on when to use this tool versus alternatives, or how to use the filtering options (e.g., whether page_id and page_name are mutually exclusive or complementary).

    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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