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zym9863

ModelScope Image MCP Server

by zym9863
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'generate_image' has a clearly distinct purpose that cannot be confused with any other tools in the set.

    Naming Consistency5/5

    With only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to create inconsistency with.

    Tool Count2/5

    A single tool for an image generation server is too minimal for the apparent scope. While image generation is a core function, typical image-related servers would include additional tools for variations, editing, or management tasks, making this feel incomplete.

    Completeness2/5

    The server is severely incomplete for an image generation domain. It only provides generation with no tools for editing images, creating variations, managing generated content, or handling different parameters or styles, which are common in such systems.

  • Average 2.6/5 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
    • Last stable release on
    • 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 full burden but offers minimal behavioral insight. It mentions ModelScope but doesn't disclose traits like rate limits, authentication needs, cost implications, or what happens during generation (e.g., polling behavior implied by parameters). This leaves significant gaps for a tool with 9 parameters.

    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 extremely concise with a single sentence, '使用ModelScope生成图片', which is front-loaded and wastes no words. However, it may be overly terse given the tool's complexity, potentially sacrificing clarity for brevity.

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

    Completeness2/5

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

    Given the tool's complexity (9 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output format, error handling, or behavioral context, making it inadequate for an AI agent to fully understand how to invoke and interpret results effectively.

    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 schema fully documents all 9 parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or usage tips. Baseline 3 is appropriate as the schema handles the heavy lifting, but the description doesn't enhance understanding.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description '使用ModelScope生成图片' clearly states the action (generate) and resource (image) but is vague about specifics. It doesn't differentiate from siblings (none exist) but lacks detail about what ModelScope is or the generation process. It's functional but minimal.

    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?

    No guidance is provided on when to use this tool versus alternatives, prerequisites, or context. The description only states what it does without any usage instructions or exclusions, leaving the agent to infer based on the tool name and parameters alone.

    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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