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Sunwood-ai-labs

Command Executor MCP Server

by Sunwood-ai-labs
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The agent can only select 'execute_command', so disambiguation is perfect.

    Naming Consistency5/5

    The single tool is named 'execute_command', following a clear verb_noun pattern, which is consistent and intuitive.

    Tool Count3/5

    One tool is minimal for a command executor. While it covers the core functionality of executing pre-approved commands, it lacks supporting tools for listing or managing commands, making the set feel thin.

    Completeness2/5

    The tool surface is severely incomplete. It only provides execution but lacks tools to list available commands, check execution status, or retrieve results, which are essential for effective agent interaction.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 0 of 2 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under ISC 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

  • Behavior2/5

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

    Without annotations, the description carries the full burden. It mentions 'pre-authorized commands' implying an authorization check, but does not disclose what happens if unauthorized, potential side effects, or return values. This is insufficient for a command execution tool.

    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 efficient. Every word earns its place with no redundancy or fluff.

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

    Completeness2/5

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

    Given the simplicity (1 parameter, no output schema), the description is minimal. It fails to cover important behavioral aspects like success/failure modes, authorization details, or examples. For a potentially powerful tool, it leaves significant gaps.

    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% with a single parameter 'command' described as '実行するコマンド' (command to execute). The tool description adds the constraint that commands must be pre-authorized, which adds meaning beyond the schema. Baseline of 3 is appropriate as the description adds some value but not extensive detail.

    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 verb 'execute' and the resource 'pre-authorized commands', distinguishing it well. It is specific about the pre-authorization constraint, which adds clarity, though no siblings exist to differentiate from.

    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?

    The description provides no guidance on when to use this tool versus alternatives, nor any when-not or prerequisites. It merely states what it does without context for appropriate usage.

    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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