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ttkkasd

Jimeng MCP Server

by ttkkasd
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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 ambiguity or confusion between tools. The single tool 'generateImage' has a clearly defined purpose that cannot be mistaken for any other tool in the set.

    Naming Consistency5/5

    The naming pattern cannot be inconsistent with only one tool. The tool name 'generateImage' follows a clear verb_noun pattern, and there are no other tools to create any inconsistency.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it offers limited functionality and may not cover the domain adequately. While it might suffice for a very narrow scope, it feels thin and underdeveloped for typical MCP server applications.

    Completeness2/5

    Based on the server name 'Jimeng MCP Server' and the tool description '调用即梦AI生成图像' (calls Jimeng AI to generate images), the domain appears to be image generation. However, with only one tool, there are significant gaps—no tools for modifying, retrieving, or managing generated images, making the surface severely incomplete for practical workflows.

  • Average 2.7/5 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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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?

    No annotations are provided, so the description carries the full burden. It mentions '即梦AI' but doesn't disclose behavioral traits like rate limits, authentication needs, output format, or error handling. This leaves significant gaps for an AI agent to understand tool behavior.

    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 a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.

    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 complexity of an image generation tool with no annotations and no output schema, the description is incomplete. It lacks details on output format, error cases, or usage constraints, making it inadequate for full contextual understanding.

    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 description adds no parameter semantics beyond what the input schema provides. With 100% schema description coverage, the baseline is 3, as the schema adequately documents parameters like 'prompt' and 'req_key'. No additional value is added by the description.

    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 '调用即梦AI生成图像' states the action (generate) and resource (image) but is vague about the specific AI service ('即梦AI') without further context. It doesn't distinguish from siblings as there are none, but the purpose is clear though not highly specific.

    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 on when to use this tool vs alternatives is provided. The description implies usage for image generation but lacks context on prerequisites, limitations, or scenarios. With no sibling tools, this is less critical, but still a gap in usage instructions.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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