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hsain9357

Explain Image MCP Server

by hsain9357
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: describe_image processes images, while list_models enumerates available models. There is no overlap or ambiguity in their roles.

    Naming Consistency5/5

    Both tools follow the identical verb_noun naming pattern (describe_image and list_models), creating a clear and predictable convention. This consistency makes the tool set easy to navigate.

    Tool Count4/5

    With only 2 tools, the server is lean but appropriately scoped for its narrow purpose of explaining images. The core describe_image tool is supported by list_models, giving just enough functionality without bloat.

    Completeness5/5

    For the stated purpose of image explanation, the server fully covers the domain. describe_image is flexible via custom prompts, supporting descriptions, OCR, object listing, and more, with no obvious gaps for a single-purpose MCP server.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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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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, the description fully bears the burden. It discloses that the tool delegates to Gemini vision, that the prompt controls output, and that image can be a local path, URL, or data URI. It does not cover edge cases like errors or rate limits, but the core behavior is clearly conveyed.

    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?

    Three sentences, each earning its place: purpose, prompt control, and input formats. It is front-loaded with the primary action and contains no redundancy or fluff.

    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 gives a solid overview for a 4-parameter tool without an output schema. It explains the flexible output behavior via the prompt and the multiple image input options. It could mention the exact return format (e.g., raw text from Gemini) but the prompt-driven nature makes this less critical.

    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%, so baseline is 3. The description repeats the image parameter format already in the schema ('local file path, an http(s) URL, or a data: URL') and adds a bit of nuance about the prompt controlling output, but does not significantly enhance parameter understanding beyond the schema.

    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: 'Look at an image and return a text interpretation from the Gemini vision model.' It uses a specific verb (look at) and resource (image), and explains the calling agent controls the prompt, distinguishing it from the sibling list_models.

    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 implies when to use: 'This is how a text-only model can see an image.' It also lists various use cases (description, OCR, list of objects, structured JSON) which gives context for usage. However, it does not provide explicit exclusions or alternatives beyond the implicit sibling distinction.

    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. The word 'List' implies a read-only operation, but it does not explicitly confirm non-mutating behavior or mention configuration requirements or error conditions, which is a minor shortfall.

    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 that conveys the purpose and scope without any filler. Every word earns its place, and no redundant information is present.

    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?

    For a zero-parameter tool, the description adequately covers the expected output (model IDs) and the endpoint context. However, it does not specify the exact return format or behavior in case of configuration or connectivity issues, leaving a slight gap.

    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 zero parameters, so the input schema is empty. The baseline of 4 applies because there is nothing to add beyond the schema; the description correctly avoids mentioning parameters that don't exist.

    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 ('List') and clearly identifies the resource ('Gemini model ids on the configured OpenAI-compatible endpoint'). This unambiguously distinguishes it from the sibling describe_image, which deals with images.

    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 there is no explicit when-to-use or alternative guidance, the context is clear: this tool should be used to enumerate available model IDs. Given the only sibling is describe_image, there is no ambiguity about when to select 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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