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1710782766

llm-vision

by 1710782766
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have clear primary purposes: describe_image handles general visual understanding and Q&A, while extract_text is specialized for OCR and structured text extraction. There is slight overlap if someone uses describe_image for text-heavy images, but the descriptions sufficiently differentiate them.

    Naming Consistency5/5

    Both tool names follow the verb_noun pattern in snake_case (describe_image, extract_text), which is consistent, predictable, and matches the server's vision-oriented domain.

    Tool Count3/5

    With only 2 tools, the server feels thin for a broad 'vision' scope. While the tools are focused and purposeful, the count is at the low end and leaves little room for a comprehensive vision toolkit.

    Completeness3/5

    The tools cover the two most common vision tasks (generic description/QA and text extraction), but many other vision capabilities (e.g., object detection, image comparison, classification) are absent. The surface is minimal and may require workarounds for non-OCR/description tasks.

  • 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
    • 25 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the basic operation (view and describe), with no mention of limitations, error handling, or side effects. The description does not go beyond the obvious purpose to add meaningful behavioral context.

    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 states the purpose and immediately explains the parameters. Every word earns its place, making it highly efficient and easy to parse.

    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's simplicity and the presence of an output schema, the description covers the core purpose and parameters. However, it lacks usage guidance and behavioral caveats (e.g., supported image formats, failure modes), so it is adequate but not fully complete.

    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?

    The description explicitly explains both parameters: 'image_path: 本地图片路径' and 'prompt: 可选的问题或指令'. This adds meaningful semantic information beyond the schema's bare property names and types, and the schema has 0% description coverage, so this compensation is valuable.

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

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool views a local image and describes content or answers questions. It identifies the resource (local image) and the action (describe/answer), but does not explicitly differentiate from the sibling tool extract_text, so it falls short of a 5.

    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?

    The description provides no guidance on when to use this tool versus the sibling extract_text. It does not mention any alternative or exclusion criteria, leaving the agent to infer usage from the basic functionality.

    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?

    No annotations are provided, so the description carries the full burden. It adds useful context about document parsing and JSON output capability via prompt, but lacks details on file format support, language limitations, or side effects. It does not indicate any destructive behavior, which is consistent with a read-only operation.

    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 comprised of two concise sentences. The first front-loads the purpose, and the second explains parameters. There is no redundancy or filler, making it highly efficient and scannable.

    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 has an output schema, so return values are communicated elsewhere. The description covers the core purpose and parameter semantics, but omits comparison with the sibling tool and potential limitations like file types or error conditions. Given the moderate complexity, it is sufficient but not exhaustive.

    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?

    The input schema has zero description coverage for parameters. The description compensates by explaining both parameters: 'image_path' as local path and 'prompt' as an optional instruction with an example (JSON output). This adds meaningful semantic value beyond the raw 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's primary function—extracting text from images—with a specific verb and resource. It further specifies support for document parsing and card key-information extraction, which distinguishes it from the sibling tool 'describe_image'.

    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?

    The description implies the tool should be used for text extraction from images, but does not explicitly state when to use it versus 'describe_image' or provide exclusions. There is no guidance on alternative tools or when not to use this one.

    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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