Skip to main content
Glama
zhoucoolboy

vision-relay-mcp

by zhoucoolboy
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v1.1.0

  • Disambiguation5/5

    With only one tool, there is no potential for confusion between tools. The tool's distinct purpose is clear.

    Naming Consistency5/5

    A single tool name does not violate any naming patterns, and it is self-consistent.

    Tool Count3/5

    One general-purpose tool can cover many vision tasks adequately, but a server dedicated to vision might benefit from having separate tools for distinct operations (e.g., OCR, Q&A) to improve clarity and agent selection.

    Completeness4/5

    The tool covers a wide range of vision tasks including OCR, extraction, description, Q&A, element location, summarization, and multi-image reasoning. Minor gaps exist (e.g., no video or generation), but the core domain is well-served.

  • Average 3.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 16 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions that images are sent together in one request and that the tool is for local images, but it fails to disclose important traits such as file size limits, supported formats, error handling (e.g., invalid paths), or what the output looks like. This is a significant gap for a general-purpose vision tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Description is concise (two sentences) and front-loads the purpose. However, it could be slightly more structured by separating the main purpose from the list of use cases. No wasted words, but the list could be more organized.

    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 has 2 parameters, no output schema, and no annotations. The description covers common use cases and the fact that images are sent together. However, it lacks details about return values, error scenarios, and file requirements. For a general-purpose tool, this is adequate but not complete.

    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% (both parameters have descriptions in the schema). The tool description adds minimal value beyond the schema: it lists use cases but does not provide specific guidance on how to craft prompts or handle image paths. Given high schema coverage, baseline 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?

    Description clearly states the tool is a general-purpose vision tool for one or more local images, listing specific use cases like OCR, extraction, description, and reasoning. Despite the generic verb 'process', the description provides clear purpose and scope, and there are no sibling tools to differentiate.

    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?

    Description explicitly lists when to use the tool (e.g., OCR, extracting information, describing content) but does not provide guidance on when NOT to use it or mention alternatives. Since there are no sibling tools, the lack of exclusion criteria is acceptable, resulting in a score of 4.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

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

Copy to your README.md:

Score Badge

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

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zhoucoolboy/vision-relay-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server