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viktor-haag

scuffed-painter

by viktor-haag
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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 split cleanly by output destination: draw_image_to_file persists to disk, while draw_image returns inline PNG content. Their names and the presence of output_path only in the file variant make misselection unlikely.

    Naming Consistency5/5

    Both tools follow the same draw_image verb-object prefix, with the file-save variant adding the clear _to_file suffix. The naming is snake_case and consistent across the whole set.

    Tool Count4/5

    Two tools is slightly below the typical 3-15 range, but the count is reasonable for a narrow painter: one output mode is file saving and the other is inline PNG. It feels minimal rather than excessive.

    Completeness4/5

    The core draw-and-output workflow is fully covered: all five shapes, validation, coordinate handling, and both file and inline delivery are present. Minor gaps remain, such as only having a fixed white background and no text or image-import tools, though these are workaround-able or outside the narrow scope.

  • Average 4.7/5 across 2 of 2 tools scored.

    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 status not available

  • This repository is licensed under Apache 2.0.

  • 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, the description carries the full burden and does so thoroughly. It discloses that parent directories are not created, invalid commands are skipped with warnings, no-valid-command fails, later commands overpaint earlier ones, drawing outside the canvas is clipped, and colors follow specific formats.

    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 long, but the length is justified by the tool's complexity and the absence of schema-level documentation. It is front-loaded with a one-sentence purpose, then organized into clear labeled sections with a concrete example.

    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?

    For a tool with four required parameters, a free-form commands array, and no annotations, the description covers nearly every decision an agent needs to make: file path rules, canvas size, command shapes, validation behavior, coordinate system, painting order, and colors. The presence of an output schema means return-value details are not required.

    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?

    The input schema provides only names and basic types, so schema coverage is effectively 0%. The description compensates completely by explaining output_path semantics, width/height meaning, command structure, all supported shape fields, defaults, and coordinate/color conventions.

    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 opens with a specific verb-resource pair: it draws a picture and saves it to a file. It also distinguishes itself from the sibling draw_image by emphasizing file output, so an agent can tell the tools apart.

    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 saving-to-file behavior implies when this tool is appropriate, but the description never explicitly states when to use it versus draw_image or what conditions would favor one over the other. This is usable but implied rather than explicit.

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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden, and it delivers thoroughly. It discloses invalid-command skipping with warnings, total-invalid-command failure behavior, coordinate origin and direction, clipping, overpainting order, color formats, and pass-through to Pillow. This is far beyond what annotations alone would provide.

    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?

    Although the description is long, the complexity of the tool justifies the length. It is well-structured with clear sections for shapes, coordinate system, colors, paint semantics, and an example, and the core purpose is front-loaded in the first sentence.

    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?

    Given no annotations, no output schema, and a highly complex nested command format, the description covers everything an agent needs: complete shape schemas, defaults, validation behavior, failure modes, coordinate system details, and a full example. Nothing essential is missing.

    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%, but the description fully compensates by explaining width, height, and commands in depth. It documents every supported shape, all their fields, defaults, color formats, coordinate semantics, and provides a working example, so no parameter meaning is left to inference.

    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 opens with a specific verb and resource: 'Draws a picture with pillow and returns the resulting image as inline PNG image content.' This clearly differentiates the tool from its sibling draw_image_to_file by emphasizing inline PNG output rather than file output, so an agent can select it correctly.

    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 gives clear context for when to use this tool: 'especially useful for (chat) applications that can directly display images.' It does not explicitly name draw_image_to_file as the alternative for file output, but the inline-image framing strongly implies that distinction, leaving little ambiguity.

    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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