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eduard256

mcp-openai-images-audio

by eduard256
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Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity or overlap in tool selection.

    Naming Consistency5/5

    With a single tool, naming consistency is inherently perfect.

    Tool Count3/5

    One tool for a domain that could benefit from separate tools (e.g., generate, edit, compose) is borderline. The tool uses modes via parameters, which is acceptable but minimal.

    Completeness4/5

    The tool covers generate, edit, and compose operations, plus background transparency. Minor gaps exist (e.g., no explicit inpainting or variations) but overall the surface is reasonable for a single-tool server.

  • 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
    • 2 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • 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

  • Behavior5/5

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

    No annotations exist, so the description fully bears the burden of behavioral disclosure. It explains mode selection, automatic model routing, output behavior (returns metadata, writes to file), and file constraints (absolute path, parent directory must exist, file must not already exist). All behavioral traits are disclosed.

    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 clear sections for first-time setup, mode selection, model routing, and output behavior. Every sentence adds value. While comprehensive, it is not overly verbose; a small improvement could be to condense the quality section slightly, but overall it is efficient.

    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 (7 parameters, multiple modes, model routing, external resource) and the presence of an output schema, the description is complete. It covers first-time reading, mode selection, parameter semantics, output handling, and edge cases (file existence, directory creation). No gaps remain.

    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 100% (each parameter has a schema description), baseline is 3. However, the tool description adds substantial value beyond the schema: it provides structured prompt guidance, detailed size use cases, quality/fidelity defaults and recommendations, background handling rules, and file path requirements. This greatly aids correct parameter usage.

    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 purpose: 'Generate, edit, or compose images via OpenAI's gpt-image family.' It further distinguishes between generation, editing, and composition modes based on the references_paths parameter. No sibling tools exist, so differentiation is not needed.

    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?

    The description explicitly advises reading a resource guide before the first call. It provides clear context for mode selection based on references_paths, model routing based on background, and when to set quality/fidelity. It covers prerequisites and usage scenarios thoroughly.

    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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