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RunhuaHuang

VisionPower

by RunhuaHuang
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v3.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of selecting the wrong tool. The tool's description clearly covers its multi-purpose nature and intended use cases.

    Naming Consistency5/5

    With a single tool named 'describe_image', the name follows a clear verb_noun pattern. There are no other tools to compare, so consistency is trivially satisfied.

    Tool Count2/5

    The server consists of exactly one tool, which the rubric identifies as too few for a server's scope. Even though the tool is multi-purpose, a one-tool server is extremely minimal and lacks the flexibility of a more complete toolkit.

    Completeness4/5

    The tool covers a broad range of image understanding tasks (OCR, description, comparison, Q&A), so it anticipates many use cases and leaves few obvious gaps. However, it may lack specialized operations for specific image processing tasks, but these are outside the stated purpose.

  • Average 4/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 72 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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?

    Annotations only include openWorldHint=true, leaving the description with the main burden. It mentions provider dependency for image_url ('support depends on the configured provider/model') and hints at performance with focused prompts, but does not disclose potential limitations (e.g., image size, errors, or untrusted-source banner details). No contradiction with annotations, but limited behavioral disclosure.

    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 a single, tight paragraph with no filler. It front-loads the core purpose and then gives practical usage tips. Slightly dense but efficient.

    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 complexity (8 parameters, no output schema), the description outlines capabilities and gives usage guidance but does not explain return formats (except via schema) or edge-case behavior. Since the schema covers parameter details and output_format, this is adequate but not rich. Missing information about size limits or error handling keeps it at a 3.

    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 description coverage is 100%, so the baseline is 3. The description adds minimal extra meaning: it emphasizes focused prompts and repeats schema guidance for image input methods (e.g., 'Use image_base64 or image_ref when URL input is unavailable'). It does not significantly enhance parameter understanding beyond 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's purpose with specific verbs and resources: 'See and understand images — screenshots, photos, diagrams, charts. Extract text (OCR), describe scenes, compare images, and answer questions about what is shown.' It distinguishes from alternatives by listing the input methods and capabilities.

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

    Usage Guidelines5/5

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

    Explicitly states when to use: 'Use whenever an image is provided via image_path, image_url, image_base64, image_ref, or images[].' Also provides guidance on how to phrase prompts for better results ('ask the specific question... instead of an open-ended...'), which is actionable usage direction.

    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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