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yuandeshoulian

ModelScope Image Generation MCP Server

by yuandeshoulian
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JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap in purpose. The tool 'generate_image' has a clear, singular function that cannot be confused with any other tool.

    Naming Consistency5/5

    Since there is only one tool, it inherently follows a consistent naming pattern. The tool name 'generate_image' uses a verb_noun format, which is clear and appropriate for its function.

    Tool Count2/5

    A single tool is too few for a server focused on image generation, as it lacks essential operations like listing models, managing generation parameters, or handling image variations. This minimal scope limits functionality and agent workflows.

    Completeness2/5

    The tool surface is severely incomplete for image generation; it only provides generation without supporting model selection, parameter tuning, or post-processing. This creates significant gaps that will hinder agent tasks requiring more control or information.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 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

  • 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 mentions that the tool 'will wait until the image is generated and return the image URL,' which adds some behavioral context about synchronous operation and output format. However, it lacks critical information about rate limits, authentication requirements, error handling, or whether the operation is read-only or mutative.

    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 efficiently structured in two sentences that directly convey the core functionality and behavioral characteristic (synchronous waiting). There's no wasted verbiage, though it could potentially be more front-loaded with key constraints.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For an image generation tool with 8 parameters and no output schema, the description provides basic operational context but lacks important details about authentication, rate limits, error conditions, and the format/structure of returned data. The 100% schema coverage helps, but the description alone doesn't provide complete guidance for effective tool invocation.

    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?

    The input schema has 100% description coverage, providing detailed documentation for all 8 parameters including ranges, defaults, and constraints. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline score of 3 for high schema coverage.

    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 the action ('generate an image') and the resource ('using ModelScope image generation models'), making the purpose immediately understandable. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.

    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?

    The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or contextual constraints. It simply states what the tool does without offering usage 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 there are no obvious security issues.
  • Evaluate tool definition quality.

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