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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The server has exactly one tool, so there is no possibility of confusing it with others. Its purpose is clearly stated and distinct.

    Naming Consistency5/5

    The single tool follows a clean verb_noun pattern (vision_analyze), which is consistent and self-explanatory.

    Tool Count3/5

    With only one tool, the server is extremely minimal and feels thin for broader workflows, though it is not trivial and adequately serves a focused purpose.

    Completeness5/5

    For a server dedicated solely to image analysis, the single tool fully covers the intended capability. There are no obvious missing operations within this narrow domain.

  • Average 3.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 7 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.

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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

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

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    Without annotations, the description carries the full burden. It discloses key behavioral aspects: external AI processing via OpenRouter, support for local paths and URLs, and text output. Still, it does not explicitly state that the tool is read-only, nor does it mention potential failure modes, network/privacy implications, or rate limits.

    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?

    Two clear sentences, front-loaded with the core purpose, followed by input and output details. No filler or redundancy; every sentence earns its place.

    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 straightforward image-analysis tool with 100% schema coverage and no output schema, the description provides adequate context: what it does, where it runs, accepted input forms, and return type. It lacks richer details like error handling or model defaults, but those are either in the schema or not critical for a simple tool.

    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 clearly documents all three parameters. The description adds minimal new meaning beyond restating that image_url can be a local path or URL and that output is text, which is already present in the schema. Baseline 3 applies.

    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 states a specific action ('Analyze an image using AI vision'), identifies the subject (image), names the underlying provider (Gemini via OpenRouter), and clarifies accepted input types and output. It fully distinguishes the tool's purpose even without sibling tools present.

    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 clearly implies when to use the tool—whenever image analysis is needed—and gives examples in the schema. However, there is no explicit guidance on when not to use it or mention of alternative tools, though no siblings are listed.

    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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