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fledgeling-co

Image Gen Pro MCP

by fledgeling-co
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Server Quality Checklist

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

  • Latest release: v0.1.6

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with other tools. The tool's description clearly delineates its multiple functions (generation, editing, style selection, SVG), but since it is the sole tool, disambiguation is trivially perfect.

    Naming Consistency5/5

    The single tool is named 'generate_image', which follows a clear verb_noun pattern. With only one tool, there is no inconsistency or mixed conventions to evaluate, making the naming fully consistent.

    Tool Count4/5

    The server has just one tool, which is on the lower end of the typical range. However, the tool is comprehensive, covering generation, editing, and multiple model options, so it justifies its presence. A slightly higher count might be expected, but the single tool is not trivial.

    Completeness4/5

    For an image generation server, the tool covers core operations: generating from text, editing with reference images, and producing SVG output. Minor gaps exist (e.g., no listing or deletion of generated images), but these are non-essential and do not block typical workflows.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 24 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

  • Behavior4/5

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

    The description adds meaningful behavioral context beyond annotations: it notes that writes to disk, that each image is billed, and that reference images must be paths on this machine (not URLs/base64). It also clarifies the SVG limitation of raster models. This goes beyond the basic readOnlyHint/destructiveHint 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 dense yet efficient, covering all essential aspects in a few sentences without redundancy. Every sentence contributes new information, and the flow from purpose to optional flags to model selection is logical.

    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 the tool's complexity (11 parameters, no output schema), the description is remarkably complete: it covers the return format (paths), the default model, style options, the SVG distinction, reference image constraints, and cost per image. It leaves little ambiguity for an agent.

    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?

    With 100% schema coverage, the baseline is 3, but the description adds valuable semantics: it explains the purpose of referenceImages, context, the svg flag override, and the billing implication of n. This helps the agent choose parameters more effectively than 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 the tool's function: generating images from a description, writing them to disk, and returning paths. It also distinguishes itself from potential alternatives by mentioning reference image support and SVG generation, making the purpose concrete and actionable.

    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 provides explicit when-to-use guidance for reference images (matching style, consistency, editing) and SVG mode (whenever the user asks for an SVG, logo, icon, or diagram). It lacks a direct 'when not to use this tool' but given no sibling tools, the context is sufficient.

    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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