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gyl1111

vision-mcp-ms

by gyl1111
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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 a single tool, there is no possibility of confusion between tools. Analyze_image is the sole operation, so selection is trivially unambiguous.

    Naming Consistency5/5

    The tool name follows a clear verb_noun pattern (analyze_image). With only one tool, consistency is inherent and the name accurately describes the operation.

    Tool Count2/5

    The server is named vision-mcp-ms, implying a broader vision scope, yet it exposes only one tool. This feels too few for the apparent domain, as typical vision MCPs offer multiple operations (e.g., OCR, object detection, image generation).

    Completeness4/5

    The analyze_image tool covers the core need of image analysis and returns text, but there are minor gaps such as no support for batch processing, no explicit model selection, or output format options. These are workarounds but leave the surface slightly incomplete.

  • Average 4.2/5 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 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

  • Behavior4/5

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

    No annotations are present, so the description carries full burden. It discloses the model-fallback mechanism, accepted input formats, and that the output is text from a vision model. It does not mention authentication, rate limits, or response details, but the output schema covers return structure.

    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?

    Two concise sentences effectively communicate purpose and key behavioral details. No redundant words; every sentence contributes value.

    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?

    The tool has an output schema, so return values are covered externally. The description handles image format and error fallback, the core complexities. The missing prompt explanation is a notable gap but does not severely undermine overall completeness.

    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 0%, so the description must explain parameters. It explains the image parameter's accepted formats (URL or base64). However, the prompt parameter is not mentioned at all, and its behavior beyond a default value is undocumented. The description adds partial value but fails to fully compensate for the schema gap.

    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 an image and return text results from a vision model. It specifies the resource (image) and the action (analyze), making the purpose unambiguous even without sibling tools.

    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?

    It provides clear context on acceptable image formats (HTTP/HTTPS URLs and base64 data URLs) and explains fallback behavior on rate limits/timeouts/5xx. However, it does not explicitly state when not to use this tool or list alternatives, though no siblings exist.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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