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DIV7NE

deepseek-vision-mcp

by DIV7NE
OverviewSchemaRelated ServersScoreDiscussions
Python
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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

  • Disambiguation4/5

    The two tools are closely related but have clearly differentiated purposes: describe_image is for general image description, while describe_ui is specifically for structured UI analysis. The description of describe_ui explicitly directs users to prefer it over describe_image for UI screenshots, reducing ambiguity.

    Naming Consistency5/5

    Both tools follow an identical verb_noun pattern: describe_image and describe_ui. The naming is perfectly consistent, with no mixing of conventions or vague verbs.

    Tool Count3/5

    With only 2 tools, the server feels slightly thin, but it serves a narrow purpose (vision description). It is on the borderline between 'too few' and 'acceptable', warranting a score of 3 rather than higher.

    Completeness4/5

    The tool surface covers general image description and a specialized UI analysis mode. Minor gaps exist, such as lack of explicit OCR or image comparison tools, but for the apparent scope of a vision description server, the coverage is reasonably complete.

  • 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
    • 10 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

  • Behavior2/5

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

    With no annotations provided, the description bears full responsibility for behavioral disclosure. It does not state whether the operation is read-only, whether it modifies anything, if there are privacy implications (e.g., uploading the image), or any limitations such as supported file formats. This lack of transparency is a significant gap for an AI agent deciding to invoke the 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 compact, using only two sentences. It front-loads the primary purpose in the first sentence and adds the optional usage detail in the second. There is no fluff or redundant repetition of schema information, making it highly concise and well-structured.

    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?

    The tool is simple with only two parameters, and an output schema exists, so the return value doesn't need explanation. However, the absence of annotations and textual disclosure of safety/behavior creates a completeness gap. Additionally, it does not address how this tool relates to the sibling 'describe_ui', leaving the agent without guidance on tool selection.

    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?

    The description explicitly explains the 'question' parameter ('ask something specific about the image instead of getting a general description'), which adds practical meaning beyond the schema. However, it never explicitly describes the 'path' parameter, leaving its format or constraints unmentioned. Given the 0% schema description coverage, this partial compensation is adequate but not thorough.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses specific language 'Look at an image file' and 'return a detailed text description' to convey the action and resource. It clearly states the tool's purpose and the optional question usage. However, it does not distinguish itself from the sibling tool 'describe_ui', so it misses the opportunity to clarify what makes it unique.

    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 provides clear context for when to use the tool: 'Look at an image file' implies visual content analysis, and it explains the optional question parameter for targeted queries. It does not explicitly mention when not to use it or compare it to alternatives, but the context is strong enough to guide typical use.

    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 burden. It thoroughly discloses the output structure (wireframe, element inventory, visual notes), making the tool's behavior clear. However, it does not mention potential input restrictions or error handling for the path parameter, which leaves a minor gap.

    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?

    Two sentences, each earning its place. The first packs in the full output format, the second provides guidance on when to use the tool. No wasted words.

    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 one-parameter analysis tool, the description provides enough context to understand what the tool returns and when to use it. The output schema exists but is not shown, so the textual description of output components is helpful. However, the lack of any explanation about the path parameter slightly reduces completeness.

    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?

    The schema has a single 'path' parameter with no description (0% schema description coverage). The tool description never mentions the path parameter, so it does not compensate for the missing schema detail. The agent must infer from the tool name and general context that 'path' refers to a screenshot file, but no explicit format or constraints are given.

    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 produces a structured UI report for a screenshot, explicitly listing the output components (ASCII layout wireframe, element inventory with positions and states, visual notes). It also distinguishes itself from the sibling tool describe_image by referencing specific use cases.

    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 says 'Prefer this over describe_image when you need layout, element positions and states rather than general prose.' This gives direct, actionable guidance on when to choose this tool over the alternative.

    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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