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HelenQ

vision-mcp

by HelenQ
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are completely distinct: describe_image handles image-to-text conversion, while configure manages server settings. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tool names are lowercase with underscores, and describe_image follows the verb_noun pattern. 'configure' is a simple verb without a noun, which is a minor deviation but still clear and predictable.

    Tool Count3/5

    The server has only two tools, which feels slightly thin for a vision-oriented MCP. However, the narrow scope of 'describe an image' plus configuration is coherent, even if minimal.

    Completeness3/5

    The core operation (image description) is covered, and configuration is available, but obvious extensions like model listing or multiple vision capabilities (e.g., OCR) are missing. It works for basic use, but the surface is quite limited.

  • 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
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior3/5

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

    No annotations exist, so the description must carry the safety disclosure. It does state that the operation writes configuration values to the .env file, which signals a mutating file write, but it doesn't specify overwrite behavior for omitted parameters, file creation, or required permissions.

    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?

    A single, front-loaded sentence that states the verb, target, and parameters with no filler or redundancy. It is appropriately sized for the simple action.

    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 description covers the core function and parameters, but it leaves ambiguity about whether an invocation with only some parameters leaves others unchanged or resets them to schema defaults. Given no output schema or annotations, this is a notable gap for a config-writing tool.

    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?

    All three parameters are fully described in the input schema (model, apiKey, baseUrl), and the description only repeats their names without adding constraints, defaults, or relationships. Schema coverage is 100%, so baseline 3 is warranted.

    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 and resource: 'Save or update the vision-mcp configuration' and names the persistence target '.env file'. This clearly differentiates it from the sibling 'describe_image', which is an image-analysis tool, so an agent can select the correct tool.

    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?

    No explicit when-to-use or when-not-to-use guidance is provided, and the sibling 'describe_image' is never referenced. Usage context is only implied by the description's purpose, so an agent must infer when configuration changes are needed.

    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?

    With no annotations, the description carries the full behavioral burden. It discloses the core behavior, accepted formats, and input source types, but does not mention output specifics, error behavior, size limits, or network implications when fetching URLs. This is adequate but not rich.

    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 two concise sentences with no filler. Essential information—purpose, model, formats, and input types—is front-loaded, making it easy for an agent to parse quickly.

    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 simple two-parameter tool with full schema coverage, the description provides enough context for an agent to invoke it correctly. The only minor gaps are unspecified constraints like file size limits or behavior on inaccessible URLs.

    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%, so the schema already documents both parameters. The tool description adds minimal semantic value beyond the schema—it restates the image path/URL constraint but does not elaborate on the prompt parameter or its effect.

    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 states a specific action ('Convert an image... into a text description'), identifies the vision model as the mechanism, enumerates supported formats, and clarifies accepted source types. This makes the tool's purpose unambiguous and distinct from the sibling 'configure'.

    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 gives clear context for when the tool is appropriate: whenever an image needs to be described as text. It also provides practical input constraints (JPEG/PNG/GIF/WebP, local path or http(s) URL), but does not explicitly discuss exclusions or compare against alternatives.

    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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Glama performs regular codebase and documentation scans to:

  • 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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