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KOG123

visual-intelligence-mcp

by KOG123
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The tool's purpose—analyzing images to answer questions or return structured data—is clear and unambiguous.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (analyze_image) that is consistent and descriptive. There are no conflicting conventions to cause confusion.

    Tool Count3/5

    A single tool feels thin for a server branded as 'visual-intelligence,' but the tool itself is versatile enough to handle various image analysis requests. The count is right at the borderline where it could use additional specialized tools, but it is not wholly inappropriate.

    Completeness4/5

    The tool covers the core need of analyzing local images and returning either descriptive text or structured JSON, including support for UI-related queries. Minor gaps exist, such as requiring local file paths and lacking support for direct image URLs, but these are workaroundable.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • 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.

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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

  • Behavior5/5

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

    Despite no annotations, the disclosure is strong: it warns about invalid short prompts, states the tool reads the image itself from a local path, and explains the meaning of json_mode for structured output. This goes well beyond the schema and gives the agent clear behavioral expectations.

    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 well-organized with a clear lead sentence followed by concise bullet-like rules, every sentence contributing value. It avoids redundancy and remains focused, making it easy to parse and apply.

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

    Completeness5/5

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

    For a simple 3-parameter tool with no output schema, the description fully covers prerequisites, call conventions, return format, and parameter behavior. It addresses all necessary context an agent would need to invoke the tool correctly.

    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?

    Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantics by emphasizing that prompt must be specific (and that short prompts are auto-supplemented), requiring absolute paths, and clarifying that json_mode enables structured JSON results. This elevates the parameter understanding beyond the schema descriptions.

    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 analyzes local images for UI automation visual assistance, using the specific verb-resource pair '分析本地图片' and explicitly mentions its return type (text or structured JSON). It also specifies the use case ('当需要查看屏幕/截图/界面时使用'), making the purpose unmistakable.

    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 provides explicit context for when to use the tool ('当需要查看屏幕/截图/界面时') and includes detailed call requirements (such as announcing the screenshot before calling and using a specific prompt). It does not mention alternatives or exclusions, but with no sibling tools specified, this is acceptable.

    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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