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uncagedspirit

Pixel Art MCP

by uncagedspirit
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Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: generating pixel art from a text prompt and pixelating an existing image. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern in snake_case: 'generate_pixel_art' and 'pixelate_image'. The naming is predictable and easy to understand.

    Tool Count3/5

    The server has only two tools, which is on the low side. While they cover basic generation and pixelation, the small number may limit the server's usefulness for complex workflows.

    Completeness2/5

    The tool set lacks common operations like listing, deleting, or editing pixel art. The completeness is minimal, covering only creation and transformation without supporting lifecycle management.

  • Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.7/5.

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

    • No community issues in the last 6 months
    • 1 commit 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.

  • 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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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 provided. Description mentions downscale+quantize but omits important behaviors: whether original image is modified, file format handling, permission requirements, or error conditions.

    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?

    Single sentence, efficient. Could be improved with structured format (e.g., bullet points for parameters) but is not verbose.

    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?

    With 5 parameters and 0% schema coverage, description is too sparse. No details on output format, error handling, or usage examples; output schema exists but is not reflected.

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

    Parameters2/5

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

    Schema description coverage is 0%; description adds no parameter-specific meaning. It loosely refers to 'downscale+quantize' but does not connect width/height/colors to those operations.

    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?

    Description clearly states it pixelates an existing image via downscale and quantize, distinguishing from sibling generate_pixel_art which generates from scratch. However, it could be more explicit about the 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 on when to use this tool vs. generate_pixel_art or other alternatives. No mention of prerequisites or context.

    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 full burden. It discloses behavioral traits such as saving to a file, path resolution against PIXEL_OUT_DIR, default dimensions and their recommended use cases, and a seed parameter for reproducibility. It does not mention overwrite behavior or error handling, but overall adds significant context.

    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 with a clear summary line followed by parameter details. It is mostly concise, though the parameter examples add length. Every sentence adds value, and the structure is easy to parse.

    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 6 parameters, 0% schema coverage, and an existing output schema, the description covers all parameters with good context. It does not detail return values, but that is acceptable since an output schema exists. The description is sufficient for an agent to understand and use the tool correctly.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description must compensate. It provides detailed explanations for all 6 parameters: prompt (example), out_path (path hints), width/height (recommended sizes for different uses), colors (palette hint), and seed (optional for reproducibility). Each parameter gains meaningful semantics beyond the schema fields.

    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 generates a pixel-art PNG from a text prompt and saves it. It specifies the verb ('generate', 'save') and resource ('pixel-art PNG'), and implicitly distinguishes from the sibling 'pixelate_image' which operates on existing images.

    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 creating pixel art from scratch but does not explicitly state when to use this tool versus the sibling 'pixelate_image'. No when-not-to-use or alternative guidance is provided, only implied context.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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