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rayner-luo

MIMO Image Recognition MCP

by rayner-luo
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity. The tool's purpose is clearly defined and distinct from any other tools.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (understand_image), consistent and predictable.

    Tool Count3/5

    One tool is borderline for an image recognition server; while it covers many tasks via a multimodal model, typical MCP servers have 3-15 tools for better granularity.

    Completeness4/5

    The tool claims to handle all image understanding tasks (OCR, description, analysis, etc.), covering the domain well. Minor gaps like specific image processing are outside scope.

  • Average 4.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 7 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.

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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?

    With no annotations provided, the description fully covers behavioral aspects: it calls the Xiaomi MIMO model, supports single and multi-image, and warns against using file-reading tools. It does not mention rate limits or auth, but the primary safety and behavioral traits are disclosed.

    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 well-structured with clear sections (English, Chinese, warning, args, returns) and is front-loaded with critical information. It is slightly verbose but every sentence serves a purpose, earning its place.

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

    Completeness5/5

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

    Given the lack of an output schema, the description includes the return value. It covers purpose, usage, parameter guidance, and warnings comprehensively. For an 8-parameter tool with no annotations, this description is exceptionally complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100% with good descriptions. The tool description adds extra context beyond the schema, such as explaining that the agent must fill the prompt and that temperature controls output randomness. This adds meaningful value for correct parameter usage.

    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 is for 'ALL image understanding tasks' and lists specific use cases like OCR, classification, description, etc. It explicitly distinguishes itself from file-reading tools, ensuring no ambiguity.

    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 when to use this tool ('whenever the user asks to...') and provides a critical warning against using other tools on image files to avoid system crash, offering clear guidance on when not to use 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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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