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zjf2671

hh-mcp-comfyui

by zjf2671
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one takes an input image for image-to-image generation, the other takes a text prompt for text-to-image generation. An agent can easily select the appropriate tool based on whether they have a reference image.

    Naming Consistency5/5

    Both tool names follow a consistent 'generate_image_from_<source>' pattern (image vs text). The naming is uniform and predictable, making it easy for an agent to infer the function.

    Tool Count4/5

    With only 2 tools, the server is very focused but slightly sparse for a general ComfyUI interface. It covers the two fundamental generation modes, which is reasonable for a minimal setup, though a few more tools (e.g., upscale or inpaint) would be expected.

    Completeness3/5

    The server covers the two core ComfyUI workflows (text-to-image and image-to-image), but lacks common operations like upscaling, inpainting, or workflow management. There are notable gaps that agents cannot work around.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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

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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 full burden. It mentions the prompt must be in English, defaults for dimensions, and returns a URL. However, it does not disclose side effects, authentication needs, rate limits, or what happens on failure. It is adequate but not thorough.

    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 one paragraph with clear Args and Returns sections. It is concise but could be slightly more streamlined. No unnecessary information, and the structure helps parsing.

    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?

    Given no output schema, no annotations, and 0% schema coverage, the description is moderately complete. It covers most parameters and the return value, but lacks usage guidance and deeper behavioral context. Additional details on seed and error handling would improve completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It explains prompt, workflow_name, width, and height, but fails to describe the 'seed' parameter, which is present in the schema but absent from the description. This omission reduces clarity for that parameter.

    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 it generates an image from a text prompt using ComfyUI. The verb 'Generates' and resource 'image' are specific, and the sibling tool 'generate_image_from_image' implies this is for text-to-image, distinguishing it effectively.

    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 guidance on when to use this tool versus the sibling. The description lists parameters but does not provide context for selection, such as specifying that this is for text-to-image while the sibling is for image-to-image. Usage is implied but not stated.

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

  • Behavior2/5

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

    No annotations are provided, and the description does not disclose behavioral traits like whether the tool is destructive, requires authentication, or handles errors. It only mentions returns a URL or error message, lacking deeper transparency.

    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 well-structured with an Args section, but could be more concise. Every sentence conveys necessary information, though the inclusion of default values could be streamlined.

    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?

    Given the tool has 5 parameters, 3 required, no output schema, and no annotations, the description covers the parameters and return value adequately. However, it lacks details on error handling, constraints, or execution behavior, making it slightly incomplete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With only 20% schema description coverage, the description compensates by explaining parameter details: prompt must be in English, workflow_name default and file extension, denoise range and effect, and seed optional. This adds value beyond the bare 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 an image using ComfyUI based on an input image, prompt, and optional parameters. This distinguishes it from the sibling tool 'generate_image_from_text' which presumably uses a text-only input.

    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?

    Description implies usage when an input image is available, but does not explicitly state when to use it vs. the sibling tool or any conditions/alternatives. No guidance on when not to use.

    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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