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rongtianjie

vision_mcp

by rongtianjie
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v1.1.0

  • Disambiguation5/5

    The two tools serve completely distinct purposes: vision_ping is a diagnostic health-check, while describe_image is the core functionality. There is no overlap, and an agent can easily tell which tool to use based on the user's intent.

    Naming Consistency3/5

    vision_ping follows a noun-verb pattern with a prefix, while describe_image uses a verb-noun pattern. Both names are clear and readable, but the inconsistent structure makes the set less predictable than a uniform convention.

    Tool Count3/5

    With only two tools, the server feels thin for a vision MCP. One diagnostic and one core tool is borderline, but it could be acceptable for a minimal, focused server. It lacks the breadth expected from a more complete toolset.

    Completeness3/5

    The server covers only image description, with no additional vision capabilities like OCR, object detection, or metadata extraction. For a dedicated vision server, this is a notable gap, but the core describe functionality is present and usable.

  • Average 3.9/5 across 2 of 2 tools scored.

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

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

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  • 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 must convey behavioral traits. It states the tool returns a test string, and the word 'Diagnostic' implies a non-destructive, read-only operation. However, it does not explicitly confirm side-effect-free behavior or mention any limitations, which is minimal for a tool with zero annotation coverage.

    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, front-loaded sentence: 'Diagnostic: return a test string to verify MCP communication.' It is concise and every word serves a purpose, with no redundancy.

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

    Completeness3/5

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

    For a simple ping tool, the description covers the core purpose and return value. An output schema exists, so return details are not needed. However, the mismatch between the parameter 'msg' and the description (which never mentions it) makes the tool incomplete for correct custom 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?

    The input schema defines one optional parameter 'msg' with a default 'ping', but schema description coverage is 0%. The description does not mention this parameter at all, so agents have no way to know they can customize the returned string. This is a significant gap for a single-parameter tool.

    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 identifies the tool as a diagnostic action: 'return a test string to verify MCP communication.' The verb 'return' and resource 'test string' are specific, and the 'Diagnostic' prefix distinguishes it from sibling describe_image, which handles image content.

    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 phrase 'to verify MCP communication' provides clear usage context. The 'Diagnostic' label implies it is for testing connectivity, not for normal image analysis. It does not explicitly exclude other use cases, but the context is sufficient for a ping tool.

    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 discloses that it returns a text description and supports common image formats, but it does not mention potential failure modes, side-effect-free nature, or behavior with invalid paths. For a simple read-only tool this is adequate but not rich.

    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 concise, with three sentences covering purpose, requirement, usage trigger, and supported formats. No redundant information; every sentence adds 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 description covers the core functionality, input requirement, formats, and when to use. With an output schema present, return value details are handled. However, optional parameters (prompt, max_tokens) are not explained, which is a minor gap.

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

    Parameters2/5

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

    Schema coverage is 0%, so the description must compensate. It explains that image_path must be an absolute local path and enumerates supported formats, but it fails to explain the `prompt` and `max_tokens` parameters, leaving their semantics unclear despite their names being suggestive.

    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 explicitly states the tool understands and describes image content, returns detailed text, and specifies when to call it (when user asks to view/understand/analyze/describe any image). This clearly distinguishes it from the sibling tool vision_ping.

    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?

    It explicitly instructs to use this tool whenever the user asks to view, understand, analyze, or describe any image, and specifies that the input must be an absolute local file path. This provides clear usage context, though it doesn't mention exclusions for non-local or URL-based images.

    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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