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FadhliRajwaaRahmana

mcp-vision-image-fadhli

by FadhliRajwaaRahmana
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.3.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: analyze_image handles image analysis, while get_usage_stats reports API usage metrics. No overlap or ambiguity exists.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: analyze_image and get_usage_stats. The naming is uniform and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin but is reasonable for a focused single-purpose image analysis service. It sits at the borderline of being too minimal.

    Completeness5/5

    For its stated purpose of image analysis via Gemini Vision, the server covers the core operation (analyze_image) and adds useful monitoring (get_usage_stats). No obvious missing operations within this narrow domain.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals that the tool uses Gemini Vision AI and accepts local paths or URLs, but it does not disclose the return format, network dependency, file size limits, or privacy implications. An agent needs more context to anticipate side effects and output shape.

    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 with no unnecessary words. Every element—verb, resource, input types, and model provider—is useful and helps the agent quickly understand and select the tool.

    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 moderately complex image analysis tool with no annotations and no output schema, the description covers the core capability and input forms but omits output format, supported file types, size limits, and network behavior. It is adequate for basic invocation but not fully complete for an agent that needs to anticipate results and constraints.

    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 coverage is 100% for all four parameters, so the schema already explains the model, prompt, image_url, and image_path in detail. The description's mention of 'file path lokal atau URL' loosely maps to the image_path and image_url parameters, but it adds no additional semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.

    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 ('Menganalisis dan mendeskripsikan') and identifies the resource ('gambar' / image), explicitly naming the supported input types (local file path or URL) and the underlying model (Gemini Vision AI). This clearly distinguishes it from the sibling tool get_usage_stats, which has a completely different purpose.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for image analysis and description by naming the input types, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference the sibling tool as an alternative. The sibling is distinct enough that confusion is unlikely, but the description itself offers no usage rules beyond the obvious.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the full burden. The verb 'Menampilkan' (displays) indicates a read-only operation, and the description clearly lists the types of data returned. It does not mention side effects, auth requirements, or rate limits, but for a simple stats display tool, this is 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, well-structured sentence that front-loads the main purpose and then lists specific statistics. It is concise with no unnecessary words, making it easy for an agent to parse quickly.

    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?

    For a tool with no parameters and no output schema, the description fully specifies what the tool does and what data it returns. The sibling tool is clearly different, and the description is complete for its simplicity.

    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?

    The tool has 0 parameters, so the schema is non-informative. The description adds no parameter details because there are none to add. Baseline for 0 parameters is 4, and the description does not need to compensate for any missing schema coverage.

    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: displaying usage statistics for the Gemini Vision API, listing specific metrics such as today's calls, total calls, remaining daily limit, per-model breakdown, and daily history. This distinguishes it from the sibling tool analyze_image, which is for image analysis.

    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 usage context is implied by the description: use when needing API usage stats. It does not explicitly exclude other tools or provide alternatives, but the clear focus on usage statistics makes the use case obvious. No explicit 'when not to use' is given, but it is not necessary given the simplicity.

    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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