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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a clear, singular purpose focused on visual review of UI edits.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'reviewEdit' follows a clear verb_noun pattern and stands alone without any conflicting conventions.

    Tool Count2/5

    A single tool is too few for a server named 'Frontend Review MCP', which suggests a broader scope for frontend review tasks. This minimal toolset feels thin and incomplete for the implied domain.

    Completeness2/5

    The tool surface is severely incomplete for frontend review. It only covers visual validation of UI edits, lacking tools for other aspects like accessibility checks, performance reviews, code analysis, or comparison of multiple edits, which are typical in this domain.

  • Average 3.4/5 across 1 of 1 tools scored.

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

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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 provided, the description carries full behavioral disclosure burden. It describes the core behavior (visual review returning yes/no with explanations) and the iterative nature ('continue to work on it'). However, it doesn't disclose important behavioral traits like processing time, file size limitations, authentication needs, error conditions, or what constitutes a valid screenshot.

    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 appropriately sized (3 sentences) and front-loaded with the core purpose. Each sentence adds value: first states purpose, second explains parameters, third describes outcomes. There's minimal redundancy, though the second sentence could be slightly more concise.

    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?

    For a 3-parameter tool with no annotations and no output schema, the description provides adequate but incomplete context. It covers the basic workflow and outcome format but lacks details about error handling, performance characteristics, or what specific visual criteria are used for evaluation. The absence of output schema means the description should ideally explain return values more thoroughly.

    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%, providing complete parameter documentation. The description adds minimal value beyond the schema: it clarifies that screenshots represent 'before' and 'after' states and mentions the edit request context. However, it doesn't provide additional semantic context about parameter relationships or usage nuances beyond what's already in the schema descriptions.

    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 states the tool's purpose: 'Perform a visual review of a UI edit request' with specific resources (before/after screenshots) and verb (review). It explains what the tool does (evaluates if edit satisfies request) and the outcome (yes/no with explanation). However, without sibling tools, it cannot demonstrate differentiation from alternatives.

    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: when you have UI edit screenshots and need validation. It mentions 'so you can continue to work on it' suggesting iterative improvement workflow. However, there's no explicit guidance on when to use this tool versus other validation methods or prerequisites for effective use.

    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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