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dehuadong

Doubao Vision MCP Server

by dehuadong
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Python
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Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    Single tool with a clear, distinct purpose: image recognition. No ambiguity with other tools.

    Naming Consistency4/5

    The tool name 'recognize_image' follows a clear verb_noun pattern. Though only one tool exists, the naming is well-structured and conventional.

    Tool Count3/5

    One tool is on the thin side for a 'Vision' server, but it serves a focused purpose. The count is borderline acceptable.

    Completeness2/5

    The server name implies broader vision capabilities, yet only image recognition is provided. Missing tools for model info, image metadata, or other vision tasks create significant gaps.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 9 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.

  • 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

  • Behavior4/5

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

    Annotations already provide readOnlyHint and openWorldHint. The description adds specific behavioral details: the exact model used (doubao-seed-2.0-pro), that it requires absolute paths or URLs, and limitations (no video/non-images). This goes beyond annotations without contradiction.

    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 (5 short sentences) with front-loaded purpose. Every sentence adds information: model, supported inputs, return, use cases, and limitations. No redundant or filler content.

    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 only 2 parameters with full schema coverage, no output schema, and annotations present, the description covers all key aspects: functionality, input formats, return, model, and limitations. It could mention output details (e.g., JSON structure) but is adequate 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?

    Schema coverage is 100% with parameter descriptions. The tool description adds extra semantics for the 'prompt' parameter, explaining that leaving it blank uses a default analysis framework and that filling it appends to the prompt and guides output structure. This adds value beyond the schema alone.

    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 uses a specific visual model to recognize image content. It specifies supported input types (local path and URL), return format (text description), and use cases. It also explicitly states what it does not support (video/non-image files), effectively distinguishing the tool even without siblings.

    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 provides when-to-use context by listing use cases like object recognition and text extraction, and explicitly states non-support for video/files. However, it lacks explicit guidance on when to prefer this tool over alternatives, and there are no sibling tools to compare.

    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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