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abyssbugg

open-vision-mcp

by abyssbugg
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v2.1.1

  • Disambiguation4/5

    Tools are mostly distinct: analyze_image is generic, while analyze_mobile_app_screenshot and analyze_webpage_screenshot target specific domains. However, an agent could potentially use analyze_image for screenshots, causing some overlap.

    Naming Consistency5/5

    All tool names follow a consistent 'analyze_<noun>' pattern, making them predictable and easy to understand.

    Tool Count4/5

    Three tools is a reasonable number for a focused vision analysis server. It is slightly on the lower end but not overly sparse, covering both general and specialized analysis.

    Completeness3/5

    The tool set covers basic image analysis and two common screenshot types. Missing operations like object detection or text extraction for general images are notable gaps, but the core analysis use case is addressed.

  • Average 3.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 56 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.

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as read-only nature, permissions, rate limits, or side effects. The description is too minimal.

    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 sentence that concisely conveys the core purpose. It front-loads the action and resource, and is easily readable.

    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 7 parameters and no output schema, the description covers the basic purpose but does not describe return values, error behavior, or how the analysis is performed. Somewhat incomplete for a tool of this complexity.

    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 baseline is 3. The description mentions supported input formats but adds no additional meaning beyond the schema descriptions. No extra value.

    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 analyzes images using a vision provider, and lists supported input formats. However, it does not differentiate from sibling tools like analyze_mobile_app_screenshot or analyze_webpage_screenshot, which are more specific.

    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?

    No guidance is provided on when to use this generic image analysis tool versus the screenshot-specific alternatives. The description lacks context for selection.

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

  • 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 disclosing behavioral traits. It only states what the tool extracts but omits details about processing, privacy, size limits, or output structure beyond what the schema implies.

    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 highly concise with two sentences, front-loading the core purpose. No unnecessary words.

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

    Completeness2/5

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

    Given 7 parameters and no output schema or annotations, the description lacks important behavioral context such as input constraints, output format details, or handling of optional parameters. It is insufficient for an agent to reliably use the tool.

    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 covers 100% of parameters with descriptions, so the description adds limited value beyond stating the extraction focus. Baseline 3 is appropriate as the schema does the heavy lifting.

    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 is for analyzing webpage screenshots and extracting content, layout, and interactive elements. However, it does not explicitly differentiate from sibling tools like analyze_image or analyze_mobile_app_screenshot, leaving the agent to infer the scope.

    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 that this tool is specialized for webpages, suggesting it should be used over the general analyze_image for that context. However, it provides no explicit when-to-use or when-not-to-use guidance, nor does it name alternatives.

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

  • Behavior2/5

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

    No annotations are provided, so the description must bear the burden of behavioral disclosure. It states the tool provides insights but does not disclose any behavioral traits such as whether it modifies anything, required permissions, rate limits, or that it is a read-only analysis. This is insufficient for an agent to understand side effects.

    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 two sentences long and front-loaded with the purpose. It is concise with no redundant words. However, it could be slightly more structured by listing the focus areas explicitly.

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

    Completeness2/5

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

    Given the tool has 8 parameters and no output schema or annotations, the description is incomplete. It does not explain the return format (default json), behavior of the platform auto-detect, or how focus areas affect the analysis. An agent would need more context to use it confidently.

    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?

    All 8 parameters have descriptions in the schema (100% coverage), so the description does not need to add much. It mentions 'insights into UI design, user experience, etc.' which aligns with the platform and focusArea parameters but does not add significant new meaning 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 it is a specialized tool for analyzing mobile app screenshots, providing insights into UI design, UX, platform conventions, and functionality. This distinguishes it from sibling tools analyze_image and analyze_webpage_screenshot, which are for general images and webpages respectively.

    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 for mobile app screenshots via 'specialized' and the listed insights, and the sibling tools provide context for when not to use (generic images, webpages). However, it does not explicitly state when to avoid this tool or provide alternatives.

    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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  • Evaluate tool definition quality.

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