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gregpriday

create-image-mcp

by gregpriday
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Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion. The tool's name and description clearly define its purpose for image generation and editing.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun convention (create_image), which is consistent and predictable. There are no other tools to create inconsistency.

    Tool Count3/5

    A single tool feels thin for a server, but it is appropriately scoped to a narrow domain of image creation and editing. It is borderline but functional.

    Completeness5/5

    The tool covers both generation and editing of images, fulfilling the stated purpose fully. There are no obvious gaps in the core lifecycle for this domain.

  • Average 3.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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.

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    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only restates the tool's purpose and the fact that it supports image input, but does not disclose side effects like writing output to files, number of generated variants, or model limitations. The rich parameter descriptions help, but behavioral transparency beyond structured schema is minimal.

    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 two sentences, front-loaded with the core action, and every sentence adds value by providing usage triggers and capability context. There is no redundant or filler content.

    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?

    The tool is complex with 12 parameters, but the schema is highly descriptive and covers all parameters with examples and defaults. The description adequately frames the high-level use cases, and the absence of an output schema and annotations is compensated by the detail in the input schema. It could mention file-saving side effects, but the output_file parameter already documents this.

    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 description coverage is 100%, so the baseline is 3. The description adds only marginal semantic context by noting that input images are used for editing and style transfer, which is already implied by the schema parameter descriptions. It does not substantially enhance parameter understanding beyond the schema.

    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 it generates or edits images using OpenAI GPT Image and lists explicit trigger phrases ('create an image', 'generate a picture', 'draw', etc.). It is specific about the verb and resource, but since no sibling tools are provided, it cannot demonstrate differentiation from alternatives.

    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 explicitly says 'Use when asked to...' and enumerates several concrete user request types. It provides clear context for when to invoke the tool, though it does not mention exclusions or alternatives because none are listed in the sibling tools.

    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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