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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'skill' loads the full instructions for a skill, while 'skill-resource' reads the files referenced by those instructions. There is no overlap in functionality, and an agent can easily tell them apart based on their descriptions.

    Naming Consistency4/5

    The tool names follow a consistent pattern with a hyphen separating words (e.g., 'skill-resource'), and both start with 'skill' to indicate the domain. However, the first tool is simply 'skill' while the second is 'skill-resource', which is a minor deviation from a strict verb_noun pattern, but overall the naming is readable and predictable.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'Skill Jack MCP' that implies managing or executing skills. This minimal set may not cover essential operations like listing available skills, creating new skills, or updating existing ones, which are likely needed for a complete skill management system.

    Completeness2/5

    Inferring the domain as skill management, there are significant gaps: no tools to list, create, update, delete, or search skills, and no way to execute or run skills directly. The provided tools only handle loading instructions and reading referenced files, leaving core lifecycle operations uncovered and likely causing agent failures in broader tasks.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 19 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 passing
  • 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?

    The description adds valuable behavioral context beyond annotations. Annotations indicate read-only, non-destructive, and idempotent operations, but the description clarifies that it 'Returns the complete SKILL.md content with step-by-step guidance, examples, and file references to follow,' detailing the output format and purpose. This enhances understanding without contradicting annotations, which already cover safety aspects like read-only and non-destructive hints.

    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 and appropriately sized, with key information front-loaded: the first sentence explains the core action and return value. The additional section '# Skills' provides necessary usage context without redundancy. Every sentence serves a purpose, making it efficient, though it could be slightly more concise by integrating the usage guidelines more seamlessly.

    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 complexity (simple read operation with one parameter), rich annotations (readOnlyHint, idempotentHint, etc.), and no output schema, the description is reasonably complete. It explains what the tool does, when to use it, and what it returns, covering essential aspects. However, it could benefit from more detail on error handling or output structure to fully compensate for the lack of output schema.

    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?

    The input schema has 100% description coverage, with the 'name' parameter documented as 'Skill name from <available_skills>'. The description doesn't add further parameter details beyond what the schema provides, such as examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to given the comprehensive schema.

    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: 'Load a skill's full instructions' and 'Returns the complete SKILL.md content'. It specifies the verb ('Load') and resource ('skill's full instructions'), making the action clear. However, it doesn't explicitly differentiate from the sibling tool 'skill-resource', which could help distinguish their specific roles.

    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 provides clear context for when to use the tool: 'When a user's task matches a skill description below: 1) activate it, 2) follow its instructions completely.' This gives explicit guidance on the trigger condition and expected workflow. However, it doesn't mention when not to use it or alternatives, such as how it differs from 'skill-resource'.

    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?

    The description adds valuable context beyond the annotations: it explains that you can read single files or entire directories at once, and provides specific examples of path usage. While annotations already indicate this is a safe, read-only operation (readOnlyHint=true, destructiveHint=false), the description enhances understanding of the tool's behavior with practical file/directory reading capabilities.

    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 perfectly concise with three focused sentences that each serve a distinct purpose: stating the tool's purpose, providing usage guidance, and explaining parameter behavior. There is zero wasted text and the information is front-loaded effectively.

    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 read-only tool with comprehensive annotations and full schema coverage, the description provides excellent context about when and how to use it. The only minor gap is the lack of output schema, but the description compensates well by explaining what the tool reads (files, directories) and providing concrete examples.

    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?

    With 100% schema description coverage, the schema already documents both parameters thoroughly. The description adds some practical context about path usage ('Pass a directory path... to read all files in that directory at once') but doesn't provide significant additional parameter semantics beyond what the schema already specifies.

    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 specific action ('Read files'), target resource ('files referenced by skill instructions'), and scope ('scripts, snippets, templates'). It distinguishes from the sibling 'skill' tool by specifying this is for reading files referenced by skills rather than general skill operations.

    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 provides explicit guidance on when to use this tool ('Use when skill instructions mention specific files to read or copy') and includes practical examples of different path scenarios. It clearly defines the context for tool invocation with concrete examples.

    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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