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aabbccddwasd

agent-vision-mcp

by aabbccddwasd
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Server Quality Checklist

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

  • Latest release: v0.2.0-hardened

  • Disambiguation5/5

    There is only one tool, so there is no possibility of ambiguity or misselection. The tool's purpose is clearly distinct and singular.

    Naming Consistency5/5

    With a single tool named 'analyze_image', the naming follows a clear and consistent verb_noun pattern. There are no other tools to introduce inconsistency.

    Tool Count4/5

    The server has only one tool, which is below the typical 3-15 range, but it is a narrow-purpose server focused on image analysis. The single tool is well-scoped and earns its place, making the count slightly under but reasonable.

    Completeness5/5

    The single tool covers a wide range of image analysis needs—description, OCR, UI element detection, chart reading, etc. There are no obvious gaps for the stated purpose of giving a non-vision model the ability to see.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • 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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that it uses a vision AI model and details supported input formats (base64 data URL, HTTP link, local file path). It doesn't disclose failure modes or rate limits, but given the annotation coverage, this is a meaningful addition.

    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 three succinct sentences: purpose, when to call, and input formats. It is front-loaded with the primary function and every sentence contributes distinct value without redundancy or fluff.

    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?

    For a simple read-only analysis tool with two well-documented parameters, the description covers the purpose, usage, and input formats thoroughly. The 'see' metaphor implies a textual output, and the examples clarify what to expect. It omits explicit return format or size limits, but those are not critical for an AI agent to invoke the tool correctly.

    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%, with both 'image' and 'prompt' clearly documented including formats, requirements, and examples. The description adds no new parameter-level information beyond what the schema provides, so it does not exceed the baseline for high coverage.

    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 uses a specific verb 'Analyze' with the resource 'image', immediately clarifying the tool's function. It goes further by explaining the purpose ('giving a non-vision main model the ability to see') and listing concrete use cases (describe, OCR, identify UI elements, read charts). There are no siblings, so differentiation is not required.

    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 explicitly states 'Call this when the user sends an image, or when you need to understand an image', providing clear trigger conditions. It also enumerates example analysis types, which helps the agent decide when to use the tool. No alternatives exist, but the guidance is strong and actionable.

    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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  • 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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