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Markusbetter

vision-mcp-server

by Markusbetter
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TypeScript
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Server Quality Checklist

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

  • Latest release: v1.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools. The name 'analyze_image' clearly describes its purpose.

    Naming Consistency5/5

    With a single tool, naming consistency is trivially satisfied. The name 'analyze_image' follows a clear verb_noun pattern.

    Tool Count2/5

    The server is named 'vision-mcp-server' implying a broad scope for vision tasks, but only one tool is provided. This is too few for the apparent scope, which would typically include at least model management and multiple analysis capabilities.

    Completeness3/5

    The tool covers basic image analysis but lacks supporting operations such as listing available models, batch processing, or other common vision tasks. These gaps can be worked around to some extent but are notable.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 4 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?

    Annotations already declare the tool read-only and non-destructive, so the description only needs to add beyond that. It discloses a key behavior: automatic switching to backup models on rate limits, timeouts, or service failures. This is valuable context not present in the annotations, with no contradictions.

    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 entire description is one concise sentence that front-loads the primary function and includes the important failover trait. Every word contributes useful information with no redundancy.

    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 two fully described parameters, a clear read-only safety profile, and a simple analysis task, the description covers the essentials. It omits the return format, but since no output schema exists, a brief note could help; however, the core functionality and robustness are well described.

    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 description coverage is 100%, with both 'image' and 'prompt' having descriptive text. The description adds no parameter-specific meaning beyond that, so the baseline score of 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 explicitly states the action ('analyze image') and the resource ('image'), with 'using a configurable vision model' adding specificity. Even without sibling tools to differentiate, the purpose is unmistakable and directly tied to the tool name.

    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 but does not explicitly state when to use this tool versus alternatives or provide exclusions. The mention of automatic failover suggests reliability, but no concrete usage scenarios or limitations are given.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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