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wchiway

Qwen Image MCP Server

by wchiway
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: generate_image creates images from text prompts, while edit_image modifies existing images using reference images and instructions. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow the exact same verb_noun pattern with clear, descriptive verbs: generate_image and edit_image. The naming is predictable and consistent.

    Tool Count4/5

    Only two tools is on the thinner side, but they cover the two core capabilities of the Qwen image model (text-to-image and image-to-image editing). The server is narrowly scoped and each tool is essential, making the count reasonable for its purpose.

    Completeness5/5

    For the domain of image generation and editing, the server provides both fundamental operations. There are no obvious missing functions like image variation or upscaling, but the core workflows of generating from text and editing reference images are fully covered.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 9 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.

  • 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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It discloses prompt_extend is enabled by default and that the model auto-recommends resolution, which are useful behavioral traits. However, it doesn't mention costs, permissions, or side effects, leaving gaps.

    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?

    Two sentences, concise and front-loaded with the core purpose. No fluff.

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

    Completeness4/5

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

    Given the rich schema with 9 parameters and output schema, the description provides sufficient overview, covering the primary function and key defaults. It doesn't cover all parameters but the schema does. Sibling differentiation could enhance completeness but isn't essential.

    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 coverage is 100%, so baseline is 3. The description adds context about prompt language support ('支持中英文提示词') and the auto-recommendation behavior relating to size, but most parameter details are already in the schema.

    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 generates images from text prompts ('文生图（T2I）— 根据文本提示词直接生成图像'), using a specific verb and resource. It implicitly distinguishes from the sibling tool edit_image, which handles editing.

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

    Usage Guidelines3/5

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

    No explicit when-to-use or alternatives are mentioned. The description implies usage for generating new images but doesn't exclude editing tasks or point to edit_image. Context about language support and auto-resolution is provided, but no comparative guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden for behavioral disclosure. It explains that images can be supplied as URL or Base64 and that the model generates an edited output based on the prompt. However, it does not mention any limitations, resource costs, or the return format—though the output schema may cover the latter.

    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 two sentences followed by a short list of use cases, immediately front-loaded with the purpose. There is no redundant or extraneous wording.

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

    Completeness4/5

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

    Given the tool's complexity (10 parameters) and the presence of an output schema, the description covers the core functionality and typical use cases adequately. It does not explain output handling, but the output schema compensates, making the description sufficiently complete for an agent to select the tool.

    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% coverage with detailed descriptions for all 10 parameters, including seed, size, watermark, and prompt_extend_mode. The description only reiterates the reference image count (1-3) and text instructions, adding little beyond what the schema already provides.

    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 identifies the tool as image-to-image editing (I2I) that uses 1-3 reference images combined with text instructions for precise editing. The mention of reference images distinguishes it from the sibling generate_image, which would not require them.

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

    Usage Guidelines4/5

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

    The description lists concrete use cases (outfit change, background change, style transfer, person-preserving editing) that clarify when this tool is appropriate. It does not explicitly state exclusions or alternative tools, but the I2I definition implies text-to-image should use generate_image.

    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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Glama performs regular codebase and documentation scans to:

  • 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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