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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool retrieves a distinct brand asset: identity, tone, visual guidelines, and mascot. There is no overlap in purpose or content, making selection unambiguous.

    Naming Consistency5/5

    All tool names follow the consistent 'get_' prefix followed by the specific asset type (identity, tone_of_voice, visual_guidelines, mascota). This is a uniform and predictable naming pattern.

    Tool Count5/5

    Four tools is well-suited for a brand information server. Each tool covers a distinct main aspect of the brand, and the count feels neither too sparse nor bloated for the domain.

    Completeness5/5

    The tool set comprehensively covers the primary dimensions of a brand identity: mission/philosophy, tone, visual style, and mascot. For a read-only retrieval server, this is a complete surface with no obvious gaps.

  • Average 4.2/5 across 4 of 4 tools scored. Lowest: 3.5/5.

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

    • No community issues in the last 6 months
    • 10 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description must convey behavioral expectations. It states 'Retrieves,' implying a read-only operation, and lists the content areas. However, it does not mention permissions, return format, or potential side effects. The tool is simple and non-destructive, so the minimal disclosure is acceptable, but richer context would be beneficial.

    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 a single, focused sentence that immediately states the purpose and content scope. It contains no filler or redundant information, making it highly concise and well-structured.

    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 no-parameter retrieval tool, the description adequately captures the essential information. However, the existence of sibling tools (get_brand_identity, get_tone_of_voice) introduces potential ambiguity about which tool to use, and the description does not address this. Adding a note about when to choose this tool would improve completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters, so the input schema fully describes the interface. With 0 parameters, the baseline score is 4, and there is no need for the description to elaborate on parameters.

    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 uses a specific verb ('Retrieves') and a clear resource ('Code-Me visual guidelines'), and further specifies the content scope (colors, typography, logos, illustration rules). It does not explicitly differentiate from siblings like get_brand_identity, but the content list provides some distinction.

    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?

    No guidance is given on when to use this tool versus the sibling tools (get_brand_identity, get_tone_of_voice, get_mascota). There are no conditions, alternatives, or exclusions mentioned, leaving the agent without information on choosing the appropriate tool.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the burden. The verb 'Retrieves' implies a safe, non-destructive read operation, which is appropriate for a getter with no parameters. It does not disclose return format or potential side effects, but for a simple getter this is acceptable.

    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 a single, front-loaded sentence with no wasted words. It efficiently conveys the tool's purpose and specific subject (Bitxo).

    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 low complexity (no params, simple getter), the description is sufficient. It explains what is retrieved (rules and personality), which serves as a proxy for return value expectations, even without an output schema.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters, so the description need not explain parameter semantics. Schema coverage is trivially 100%, and the baseline for zero params is 4.

    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 verb 'Retrieves' and specifies the resource as 'Code-Me mascot rules and personality (Bitxo)'. This distinguishes it from sibling tools like get_brand_identity and get_tone_of_voice, which target different aspects of brand assets.

    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 for retrieving mascot-related information but does not explicitly state when to use this tool over alternatives or provide exclusions. With sibling tools present, explicit guidance would improve clarity.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the burden. It clearly communicates a read-only operation via 'Retrieves', and the purpose adds context about what the data represents. It doesn't delve into return formatting or side effects, but for a simple getter, this is acceptable.

    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 sentences, no redundancy. The first sentence states the core function, the second explains the intended use. Very efficient and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a no-parameter, read-only tool with a clear purpose, the description is complete. It lists the key components of the brand identity and explains when to use it, covering all necessary context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters, so the baseline is 4. The description adds no parameter details because none exist; the schema already covers everything (empty 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 it retrieves the Code-Me brand identity, listing specific elements (naming, philosophy, tagline). This distinguishes it from sibling tools like tone of voice, visual guidelines, and mascota.

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

    Usage Guidelines4/5

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

    It gives clear context for when to use this tool: to understand the core mission and vision. It doesn't explicitly mention alternatives or when not to use it, but the guidance is sufficient for the intended use case.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries full responsibility for behavioral disclosure. It clearly indicates the operation is a read-only retrieval ('Retrieves'), and specifies the content scope (tone of voice rules). For a zero-parameter getter, this is adequate transparency without hidden 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two concise sentences with the primary purpose front-loaded ('Retrieves...') and usage guidance immediately following. No redundant or filler content, every sentence earns its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple zero-parameter retrieval tool with no output schema, the description is complete: it states what the tool returns and how to use it. No missing information that would impair selection or invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has 0 parameters, so the description does not need to add parameter-level details. Per the baseline for 0 params, a score of 4 is appropriate because the empty schema is entirely self-explanatory.

    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 uses a specific verb ('Retrieves') with a clear resource ('Code-Me tone of voice rules and language guidelines'). It distinguishes from sibling tools like get_brand_identity and get_visual_guidelines by focusing on tone/language, making its purpose unambiguous.

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

    Usage Guidelines4/5

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

    The description explicitly states when to use the tool: 'Use this to validate or generate text for different audiences (parents, students, teachers).' This provides clear context for usage, though it does not explicitly call out alternatives or exclusions, which would be a 5.

    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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