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yanickxia

agent-vision

by yanickxia
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are perfectly distinct: analyze_image handles static images, while analyze_video handles moving media via keyframes. There is no overlap in input types or use cases, so an agent can easily choose the correct tool.

    Naming Consistency5/5

    Both tools follow the exact same verb_noun pattern: analyze_image and analyze_video. This consistent naming makes the tool set predictable and easy to navigate.

    Tool Count4/5

    With only two tools, the server is minimal but well-scoped for its stated purpose of visual media analysis. While it's on the low end, the narrow domain justifies the count, and each tool covers a major media type.

    Completeness5/5

    The server covers the two essential types of visual input—images and videos. Both tools are generic enough to handle a wide range of analysis tasks (screenshots, OCR, UI flows, etc.), leaving no obvious gaps in the covered domain.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 10 commits in the last 12 weeks
    • 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

  • 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 specifies input formats but does not explain what the tool returns (e.g., an answer to the prompt, a description, or analysis results) or any side effects, privacy implications, or limitations. This is a significant gap for a tool that sends image data to an external service.

    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, consisting of two sentences that immediately state the purpose and then list use cases. It avoids unnecessary repetition and is well-structured, with the action and input types front-loaded.

    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?

    This tool has no output schema and no annotations, so the description should compensate by explaining what the tool returns. It fails to mention the prompt parameter or that users can ask specific questions about the image, making the tool's behavior incomplete for an agent deciding how to invoke it. The description covers the input side well but leaves the output side undefined.

    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 input schema already covers both parameters (source and prompt) with clear descriptions, and schema coverage is 100%. The tool description adds context about use cases but does not add meaning beyond the schema for the individual parameters, so the baseline score of 3 is appropriate.

    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 function with a specific verb ('Analyze') and resource ('an image'), and enumerates concrete use cases (screenshots, UI, OCR, diagrams, photos, visual debugging). This distinguishes it from its sibling analyze_video, which focuses on video.

    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 usage context by listing supported input types (local path, HTTP(S) URL, base64 data URI) and concrete scenarios where the tool should be used. However, it does not explicitly state when not to use it or mention the alternative analyze_video for video content, though this is implied by the sibling name.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the burden of behavioral disclosure. It transparently reveals the keyframe-extraction mechanism and source types (local/remote). It does not mention output format or potential limitations (e.g., loss of temporal context between frames), which prevents a top score, but the core behavior is well outlined.

    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 exceptionally concise: two sentences, no fluff, with the primary purpose front-loaded and supporting usage guidance in the second sentence. Every word earns its place.

    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?

    The tool has no output schema, so the description should clarify the return value. While the description explains the mechanism (extracting keyframes) and gives usage examples, it does not state what the analysis produces (e.g., text summary, labeled frames, etc.), leaving a notable gap for an AI agent selecting and invoking 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 input schema provides detailed descriptions for all three parameters (source, prompt, max_frames), achieving 100% schema coverage. The description adds no extra parameter-level meaning beyond what the schema already states, so the baseline score of 3 applies.

    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 function: 'Analyze a local or remote video by extracting evenly spaced keyframes.' This specific verb+resource+method distinguishes it from typical image tools. It also lists concrete use cases like 'screen recordings, UI flows, demos, and event summaries,' further clarifying its video-centric purpose.

    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 phrase 'Use this for screen recordings, UI flows, demos, and event summaries' offers clear, context-rich guidance on when to use the tool. However, it does not explicitly mention when not to use it or compare it with the sibling tool 'analyze_image,' so it stops short of full alternative guidance.

    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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