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RuyimgByCN

mcp-vision-server

by RuyimgByCN
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have entirely distinct purposes: analyze_image for image understanding and list_providers for listing provider presets. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (analyze_image, list_providers), making naming predictable and clear.

    Tool Count3/5

    Only two tools for a vision server feels slightly thin. While the count is not extreme, it borders on insufficient for typical vision tasks.

    Completeness2/5

    The server covers only basic image analysis and a provider listing, missing common vision operations like generation, manipulation, or filtering. The tool surface is significantly incomplete.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 16 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

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 for behavioral disclosure. It states the tool can 'analyze' an image but does not reveal that it likely calls an external API (implied by parameters like api_key, base_url, provider), nor does it describe side effects, idempotency, or return behavior beyond the output schema.

    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 very concise: two sentences summarizing purpose and use cases. It is front-loaded and wastes no words. However, it could be slightly more structured (e.g., separate parameter notes) to improve readability.

    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?

    Given the tool's complexity (9 parameters, output schema exists, sibling tool), the description is incomplete. It lacks parameter guidance, input/output format details, and prerequisites (e.g., API key requirements). The output schema exists but is not referenced. The description should provide more context for accurate invocation.

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

    Parameters1/5

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

    Schema description coverage is 0%, yet the description provides no explanation for any of the 9 parameters (image, model, prompt, api_key, base_url, provider, image_type, max_tokens, temperature). It fails to add meaning beyond the schema, leaving the agent to guess 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's purpose: 'Analyze or understand an image.' It provides specific use cases (image understanding, screenshot analysis, OCR, visual Q&A, UI restoration) which distinguishes it from the sibling tool 'list_providers' and gives a precise verb-resource relationship.

    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 explicitly lists when to use the tool (e.g., when image understanding or OCR is needed) and implies a specific context. However, it does not mention when not to use it or suggest alternative tools, which would improve guidance.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It only states output format but omits any behavioral details like performance, rate limits, or data freshness. However, for a simple parameterless list tool, the minimal description is somewhat adequate.

    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 a single short sentence that is front-loaded with the key action and resource. Every word earns its place, with no redundancy.

    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?

    Given the tool has no parameters and an output schema is present, the description is nearly sufficient. However, it does not elaborate on what 'provider presets' entail, which could be slightly ambiguous. The completeness is high but not perfect.

    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?

    There are zero parameters, so the description adds no information beyond the empty schema. According to the rubric, 0 params has a baseline score of 4.

    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 verb 'List', the resource 'all supported provider presets', and the output format 'as a JSON array'. It effectively distinguishes from the only sibling tool 'analyze_image'.

    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?

    While no explicit when-to-use or alternatives are given, the context with only one sibling tool 'analyze_image' makes the usage obvious. The description lacks exclusion criteria but remains clear for this simple 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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