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huangmiuXyz

Jimeng MCP Server

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generateImage' has a clearly distinct and singular purpose, making it impossible for an agent to confuse it with other tools in this set.

    Naming Consistency5/5

    The naming is trivially consistent as there is only one tool. The tool name 'generateImage' follows a clear verb_noun pattern, and with no other tools to compare against, it cannot exhibit any inconsistency in naming conventions.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and suggests a narrow scope. For an image generation server, this might be acceptable if it's highly specialized, but it feels thin and could indicate an incomplete surface for broader AI tasks.

    Completeness1/5

    The server is severely incomplete for any meaningful domain beyond basic image generation. With only one tool, there are significant gaps in coverage—no ability to edit, delete, list, or manage images, and no support for other AI tasks. This will likely cause agent failures in multi-step workflows.

  • Average 1.7/5 across 1 of 1 tools scored.

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

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior1/5

    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 but provides none. It doesn't mention whether this is a read or write operation, what permissions might be required, rate limits, costs, response format, or any behavioral characteristics. The description is completely silent on all behavioral aspects beyond the basic action implied by the name.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    While technically concise with just one short phrase, this represents under-specification rather than effective conciseness. The single sentence doesn't earn its place by providing meaningful information - it's essentially just a translation of the tool name. Good conciseness balances brevity with information density, which this description fails to achieve.

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

    Completeness1/5

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

    For a complex image generation tool with 9 parameters, no annotations, and no output schema, the description is completely inadequate. It provides no information about what the tool returns, how to interpret results, error conditions, or any context needed to use the tool effectively. The agent would have to rely entirely on the input schema with no guidance about the tool's purpose or behavior.

    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 schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no parameter information whatsoever, so it neither compensates for gaps nor adds value beyond the schema. This meets the baseline of 3 when the schema does all the work, but the description contributes nothing additional.

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

    Purpose2/5

    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' is a tautology that essentially restates the tool name 'generateImage' in Chinese. It provides no additional specificity about what kind of image generation this is, what model or service it uses, or what distinguishes it from other image generation tools. While it does include the verb '生成' (generate) and resource '图像' (image), it lacks any meaningful differentiation or detail.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides absolutely no guidance on when to use this tool, what scenarios it's designed for, or any prerequisites or constraints. There are no sibling tools mentioned, so differentiation isn't required, but the description fails to give any context about appropriate use cases, limitations, or alternatives.

    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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  • Evaluate tool definition quality.

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