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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The server exposes only a single tool, so there is no possibility of confusion between tools. Its purpose is clearly defined by the description.

    Naming Consistency5/5

    With one tool, naming is trivially consistent. The 'vision.inspect' name follows a clean namespace.action pattern.

    Tool Count2/5

    Providing only one tool makes the server feel very thin. While the tool is potent, the surface area is likely too small for a general-purpose vision MCP server, suggesting a need for at least a few complementary tools.

    Completeness4/5

    The tool fully covers image inspection with configurable modes and rigor levels, but there is no way to list available images or retrieve metadata, which could be considered minor gaps depending on the intended workflow.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 57 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 failing
  • This repository is licensed under MIT License.

  • 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

  • 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 discloses security gating ('access is restricted by the configured allow/deny roots'), mode auto-selection behavior, and rigor levels including an independent verifier for 'critical' and a fallback route for 'cheap'. This is substantial behavioral context, though it stops short of describing the exact output format or failure handling, which would push it to 5.

    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 well-organized: a clear intro, a usage guideline sentence, and a structured Args list. Every sentence provides necessary information without redundancy. It is appropriately sized for the tool's complexity.

    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?

    The description covers purpose, usage, and parameter semantics thoroughly. However, since there is no output schema, it would be beneficial to describe the return format ('compact visual evidence') in more detail or include examples. This gap prevents a perfect completeness score, but the overall context is strong.

    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?

    Schema description coverage is 0%, so the description must compensate, and it does. Each parameter is explained with meaningful detail: paths must be absolute and at most four, query should be exact and exclude unrelated context, mode auto selects compare for two images, and rigor explains the backend/verifier/fallback behavior. This adds significant value beyond the raw schema.

    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's function: 'Inspect local images and return compact, security-gated visual evidence.' It specifies the resource (local images) and the action (inspect), and differentiates when to use it versus alternatives by listing conditions like 'correctness depends on visible pixels...' This is a specific, concise purpose statement.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly says 'Use this only when correctness depends on visible pixels, image-only text, layout, charts, diagrams, or visual before/after comparison. Prefer source text or structured data when those are sufficient.' This provides clear when-to-use and when-not-to-use guidance, effectively distinguishing it from other tools that might handle text or structured data.

    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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