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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap with other tools. The tool's purpose is singular and clearly defined as generating O'RLY? book covers.

    Naming Consistency5/5

    The single tool name 'generate_orly_cover' follows a clear verb_noun pattern, and there are no other tools to create inconsistency. The naming is straightforward and descriptive.

    Tool Count2/5

    A single tool is too few for a server's purpose, as it limits functionality and suggests an incomplete or overly narrow scope. Typically, a well-scoped server should have 3-15 tools to cover a domain adequately.

    Completeness3/5

    The tool provides a specific function (generating book covers) with extensive customization options, but the server lacks related operations such as listing, editing, or managing covers. This creates a notable gap in the surface for a book cover generation domain.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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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    {
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      "maintainers": [
        "your-github-username"
      ]
    }

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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 effectively discloses key behaviors: the tool creates an image, displays it directly in chat, includes default values for optional parameters, and provides resolution details. It doesn't mention rate limits, authentication needs, or error conditions, but covers the core functionality well.

    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-structured with a clear purpose statement followed by detailed parameter explanations and return information. It's appropriately sized for an 8-parameter tool, though the parameter section is lengthy. Every sentence adds value, but it could be slightly more front-loaded.

    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?

    Given the tool's moderate complexity (8 parameters, no output schema, no annotations), the description is quite complete. It explains what the tool does, all parameters, and the return format. It lacks details on error handling or advanced usage scenarios, but covers the essentials effectively.

    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 fully. It does so by explaining all 8 parameters in detail, including their purposes, default values, and effects (e.g., scale factor impacts resolution). This adds significant meaning beyond the bare 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 purpose: 'creates a parody book cover in the style of O'Reilly books with custom title, subtitle, author, and styling options.' It specifies the exact resource (O'RLY? book cover image) and action (generate/creates). With no sibling tools, this level of specificity is excellent.

    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 through the tool's purpose (generating parody book covers) but doesn't explicitly state when to use it versus alternatives. With no sibling tools, there's no need for differentiation, but it lacks explicit guidance on scenarios or prerequisites for 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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