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dclddb

deepseek-vision-mcp

by dclddb
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    There is only one tool, so there is no possibility of confusing it with another tool. Its purpose is clearly scoped to image analysis.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern and there are no conflicting naming conventions to cause inconsistency.

    Tool Count4/5

    One tool is at the low end, but it is justified for a narrow vision-analysis server. The built-in full/OCR modes keep it reasonably well-scoped rather than feeling empty.

    Completeness5/5

    For the stated purpose of providing image understanding to a non-vision model, the tool covers both general visual description and explicit OCR text/table extraction. There are no obvious missing core operations for this narrow domain.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 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

  • 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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses the two operational modes and their behavior (full visual understanding vs. OCR text extraction), which is sufficient for a read-only image analysis tool.

    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 and well-structured: trigger conditions, usage caveat about native vision, and mode selection guidance are all front-loaded. No sentence is wasted, and the note about native vision is essential for correct agent decision-making.

    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?

    The description covers the core context: what the tool does, when to invoke it, how to choose modes, and the required parameter. An output schema exists, so the return format does not need explanation. The tool is simple with only two parameters, and the description is complete for correct invocation.

    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%, so the baseline is 3. The description adds value by explaining when to use mode=ocr versus mode=full, which goes beyond the schema's brief mode description. The path_or_url parameter is already well-covered by 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 purpose: when the user provides an image, screenshot, chart, or photo, call the tool to obtain a visual content description. It uses a specific verb and resource, and the note about native vision distinguishes its intended role from the model's own capabilities.

    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?

    The description explicitly defines when to use the tool (when the model cannot directly view images) and when not to use it (if the model has native vision). It also provides clear mode selection guidance: use mode=full by default, and mode=ocr only for explicit text/table extraction.

    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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deepseek-vision-mcp MCP server – quality and maintenance score on Glama

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