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JesonMrLiu

claude-image-recognition-mcp

by JesonMrLiu
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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

    With only a single tool, there is no possibility of confusion between tools; the tool's name and description clearly define its purpose.

    Naming Consistency5/5

    The tool name follows a clear verb_noun convention (recognize_image), and since there is only one tool, naming consistency is trivially maintained.

    Tool Count3/5

    The server exposes a single tool, which is on the edge of being too few for a typical MCP server. While the tool is substantive and not trivial, the surface area feels thin compared to servers that offer multiple related operations.

    Completeness4/5

    The tool covers the core operation of image recognition/analysis with flexible input sources (path, URL, clipboard). Minor gaps might include additional controls or metadata, but the primary workflow is well-covered.

  • Average 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.

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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 the transparency burden. It discloses that the tool uses an OpenAI-compatible vision model and returns a textual answer, implying an external network call. However, it does not mention error behavior, side effects, or privacy implications of sending images to a remote model, which would be relevant for a tool like this.

    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 two sentences, front-loaded with the action, and every sentence conveys necessary information without any fluff. It is a model of concise, effective tool definition.

    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 5 parameters and no output schema, but the description explains the core purpose, accepted image sources, and return type. It does not describe the other parameters in detail, but the schema does, and for a moderately complex tool this is adequate and slightly above average.

    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 the baseline is 3. The description primarily restates the image parameter's allowed formats (local path, URL, clipboard), which adds minimal value over the schema. It does not elaborate on the other parameters beyond what the schema already provides.

    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 'Recognize / analyze' plus resource 'an image', and clearly states the supported input formats and return type. It unambiguously identifies the tool as an image analysis tool, even though no sibling tools exist to distinguish from.

    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?

    There is no explicit when-to-use vs alternatives, but since no sibling tools are provided, the context is clear. The description implies its use for analyzing images via a vision model and lists accepted input sources, giving sufficient guidance for when to choose this tool.

    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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