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bcdxc

visual-understand-mcp

by bcdxc
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.2

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools.

    Naming Consistency5/5

    The single tool 'understand_image' follows a clear verb_noun pattern, which is consistent and intuitive.

    Tool Count2/5

    With only one tool, the server feels thin for typical use cases; a broader domain like visual understanding might benefit from more specialized tools.

    Completeness5/5

    The tool's comprehensive description covers recognition, analysis, OCR, description, and comparison of both single and multiple images, leaving no obvious gaps.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 5 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

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  • 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?

    No annotations are provided, so the description carries full burden. It states that the tool returns text from a visual model, but does not disclose potential limitations (e.g., model accuracy, supported image formats, size limits) or behavioral traits like error handling. The warning against text tools adds some transparency, but overall it's minimal beyond the core function.

    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 concise with no wasted words. It front-loads the purpose and usage guidance in a few sentences, then lists parameters in a structured Args block. Every sentence adds value, and the structure is easy to scan.

    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?

    Given the tool has an output schema (not shown but indicated), the description does not need to detail return values. It covers the main purpose, usage, and all parameters. However, it lacks details on the visual model's capabilities (e.g., supported image formats, size limits, or failure modes). This is a minor gap but the description is still fairly complete for a tool with no siblings.

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

    Parameters5/5

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

    The description has 0% schema description coverage, but the Args section in the description compensates fully. It explains each parameter: prompt with examples ('extract text in image', 'analyze error info in screenshot'), image_path as local path, image_url as URL or base64, and their plural counterparts. This adds meaningful semantics beyond the schema's types and defaults.

    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 that the tool calls a visual model to understand images and returns text. It lists specific use cases (recognition, analysis, OCR, description, comparison) and explicitly distinguishes itself as the only tool that can 'see' images, contrasting with text-based tools like Read/cat.

    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?

    The description provides explicit guidance on when to use the tool ('when the user asks to recognize, analyze, OCR, describe, or compare images or screenshots') and when not to ('do not use Read/cat and other text tools to open image files'). It asserts that this is the only tool for such tasks, giving clear usage boundaries.

    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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