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Amengclass

Vision MCP

by Amengclass
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Server Quality Checklist

67%
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  • Latest release: v1.1.0

  • Disambiguation4/5

    The two tools have clear but slightly overlapping purposes: describe_image offers general visual understanding, while ocr_image specifically extracts text. An agent could theoretically use describe_image for text extraction, but the OCR tool is more direct and precise for that task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (describe_image, ocr_image), making the tool names predictable and easy to select.

    Tool Count3/5

    With only 2 tools, the server feels minimal, but it covers the essential tasks of visual understanding and OCR. It's borderline but not unreasonable for a purpose-built vision server.

    Completeness4/5

    The two tools cover the core needs of image understanding and text extraction, and describe_image is versatile enough to handle many query types. Minor gaps exist, such as no dedicated tools for image comparison or object detection, but these can be handled through describe_image.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • Last stable release on
    • 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.

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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 behavioral traits like supporting one or more images and outputting line-by-line according to original layout, which adds useful context. However, it does not mention edge cases like image quality requirements, error handling, or language support. This is adequate but not rich.

    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 concise sentence that packs all essential information: purpose, input type, and output behavior. No wasted words, front-loaded with the core function.

    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?

    For a simple one-parameter tool with no output schema, the description adequately covers purpose, input, and output format. It could mention possible limitations or error conditions, but the provided detail is sufficient for typical usage. No output schema means the description's mention of line-by-line output is valuable.

    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?

    There is only one parameter (image_paths) and its schema description already covers path formats (absolute, relative, ~). The description adds no additional parameter-specific semantics, but the schema does the heavy lifting. Baseline 3 is appropriate.

    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's function: extract all text from one or more images via OCR, and output it line-by-line preserving original layout. The verb 'extract' and resource 'images' are specific, and the output format detail distinguishes it from the sibling tool 'describe_image', which focuses on visual description rather than text extraction.

    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 implies usage context: use this tool when you need OCR text from images. However, it does not explicitly mention when to prefer describe_image or exclude non-OCR scenarios, leaving some ambiguity. The distinct purpose helps, but no explicit alternatives are named.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden. It transparently states that the tool can perform visual understanding and answer arbitrary questions, and it supports one or more images. It does not hide side effects because there appear to be none; however, it does not detail limitations like image format support or potential model errors, though these are not critical.

    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, front-loaded sentence that efficiently states the tool's purpose and provides relevant examples without unnecessary filler. Every phrase contributes value.

    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?

    The tool is relatively simple, and the description covers the core purpose, examples, and flexibility of questions. The schema provides complete parameter details, so the description need not explain them again. A minor gap is the lack of explicit differentiation from the sibling tool, but the description is still sufficiently complete for an agent to use it correctly.

    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 already documents both parameters (image_paths and question) with clear descriptions, achieving 100% coverage. The description adds minimal extra meaning beyond the schema, only emphasizing that the question can be arbitrary. Therefore, a baseline of 3 is appropriate.

    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 performs visual understanding on images and allows arbitrary questions, with concrete examples like describing content, recognizing objects, analyzing charts, and understanding screenshots. This distinguishes it from the sibling tool ocr_image, which is likely text-focused.

    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 gives clear context for when to use the tool—any visual understanding task with arbitrary questions—and lists several use cases. It does not explicitly name alternatives or exclusions relative to ocr_image, but the examples imply broad applicability beyond simple OCR.

    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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