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OnurGumus

3dpacking-mcp

by OnurGumus
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The single tool's purpose is clearly defined in its description.

    Naming Consistency4/5

    The tool name 'pack_shipment' follows a clear verb_noun pattern, which is a good convention. However, with only one tool, there is no broader pattern to assess for consistency.

    Tool Count3/5

    A single tool for a domain like 3D packing feels thin, as users might expect additional capabilities such as container management or multi-shipment optimization. The count is at the low end of the borderline range.

    Completeness3/5

    The tool covers the core packing operation comprehensively, but the server lacks auxiliary tools for activities like validating container specifications or comparing alternative packing strategies. This leaves some gaps in the overall workflow.

  • Average 4.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 7 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.

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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

  • Behavior5/5

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

    With no annotations provided, the description carries full responsibility. It discloses the tool uses a solver, returns container needs, fullness, non-fitting items, and an interactive 3D plan. It also warns about volume overestimation, providing valuable behavioral context without contradicting any annotations.

    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 concise: two paragraphs, each serving a distinct purpose. The first explains functionality and output, the second gives usage guidance. Every sentence adds value, with no redundancy or fluff.

    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?

    No output schema is present, but the description explicitly lists returned information (containers needed, fullness, non-fits, link to 3D plan). It also covers input requirements and constraints, making it contextually complete for a tool of this complexity.

    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?

    Schema coverage is 100%, so the baseline is 3. The description adds value by explaining how to phrase the 'prompt' parameter (quantities, dimensions, weights, constraints) and provides examples. However, the 'speed' parameter is only described in the schema, not in the main description, so it doesn't fully enhance that parameter's semantics.

    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 function: 'Work out how a shipment fits into shipping containers, trucks or pallets' using a 'real 3D bin-packing solver'. It specifies the resource (shipment into containers/trucks/pallets) and the action (work out), making it distinct from generic estimation tools.

    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?

    Explicitly tells when to use this tool: 'Use this instead of estimating from volume', and contrasts with volume arithmetic. It also provides guidance on input description with examples and constraints, making the usage context clear.

    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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