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kdpa-llc

local-skills-mcp

by kdpa-llc
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TypeScript
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Server Quality Checklist

67%
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  • Latest release: v1.0.3

  • Disambiguation5/5

    Each tool has a distinct purpose: validate_skill validates SKILL.md files, get_skill loads skill instructions, evaluate_skill runs an evaluation loop. No overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (evaluate_skill, get_skill, validate_skill), making the API predictable.

    Tool Count4/5

    Three tools is slightly minimal for a skill management system, but covers core operations. A few more (e.g., create_skill, list_skills) could improve completeness without being excessive.

    Completeness2/5

    Missing basic lifecycle operations: no tool to create, update, or delete skills. The get_skill tool lists available skills but this is atypical. Validation and evaluation alone are insufficient for managing a skill inventory.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 33 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.

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

    Then . Browse examples.

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

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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

  • Behavior2/5

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

    No annotations are present, so the description must carry the full burden. It does not disclose whether the tool is read-only or has side effects, what happens if the file is missing, or any required permissions. The description only states it validates and returns errors/warnings, lacking critical behavioral context.

    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 sentence that is front-loaded and to the point. It contains no unnecessary words or filler, and every part of the sentence adds value.

    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 simple tool with one parameter and no output schema, the description is fairly complete. It explains the function and the type of output (errors/warnings). However, it could mention whether the file must exist or what rules are checked, but these are minor omissions.

    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 single parameter 'skill_name' has 100% schema coverage, and its description in the schema matches the tool description. The tool description adds no additional meaning beyond what the schema already provides. Baseline 3 applies since schema coverage is high.

    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?

    Description clearly states the tool validates a SKILL.md file against specific rules and returns errors/warnings. The verb 'validates' and resource 'SKILL.md' are precise, and the output ('errors + warnings') is specified. It distinguishes from siblings like 'evaluate_skill' and 'get_skill' by focusing on validation.

    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 provided on when to use this tool versus alternatives like evaluate_skill or get_skill. There is no mention of prerequisites, context, or when not to use it. The description only states what it does, leaving the agent to infer usage.

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

  • Behavior3/5

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

    No annotations provided, so description must cover behavioral traits. It mentions prerequisites and legacy requirements, but does not disclose side effects, performance impact, or whether the tool modifies any files. This is a moderate disclosure.

    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?

    Description is a single sentence with a parenthetical, efficiently conveying the primary action and key requirements. Slightly dense, but no superfluous content; could be restructured for better readability.

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

    Completeness3/5

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

    With 10 parameters (all optional except one), no output schema, and no annotations, the description provides essential prerequisites but lacks details on output behavior and eval loop outcomes. Adequate but not comprehensive.

    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 coverage is 100%, so each parameter already has a description. The tool description adds prerequisite context but does not enhance understanding of individual parameters beyond the schema. Baseline 3 is appropriate.

    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?

    Description clearly states 'Runs Anthropic skill-creator eval loop for a skill', specifying the exact action and target resource. This distinguishes it from sibling tools get_skill and validate_skill, which have different purposes.

    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?

    Description lists prerequisites (Python, Claude CLI auth, eval set JSON) which guide usage. It implies usage for evaluation, though lacks explicit when-to-use vs siblings or when-not-to-use scenarios.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It implies a read operation loading instructions but does not disclose behavioral traits such as idempotency, permission requirements, or potential 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.

    Conciseness4/5

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

    The description is structured with a general purpose paragraph followed by a list of specific skills. It is front-loaded with the main action but slightly longer than necessary.

    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 no output schema and one parameter, the description adequately covers what the tool returns (detailed instructions) and how to call it, including examples. Lacks info on error cases but sufficient for a simple retrieval.

    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 input schema has 100% coverage with a single string parameter described. The description adds value by listing available skill names, going beyond the schema alone.

    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 loads specialized expert prompt instructions and transforms capabilities for specific tasks, distinguishing it from siblings evaluate_skill and validate_skill which assess skills.

    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?

    Provides explicit when-to-use guidance ('when you need focused expertise') and lists specific skill examples, but does not explicitly state when not to use or contrast with alternative tools.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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