Skip to main content
Glama
ah-wq

MCP Vision Relay

by ah-wq
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation1/5

    The two tools are essentially identical in purpose—both describe or analyze images using multimodal capabilities, differing only in the underlying model (Gemini vs. Qwen). An agent would have no clear basis to choose one over the other based on their descriptions, leading to confusion and misselection.

    Naming Consistency5/5

    The tool names follow a perfectly consistent pattern: both use a clear 'model_verb_noun' structure (gemini_analyze_image, qwen_analyze_image). This consistency makes it easy to understand what each tool does at a glance.

    Tool Count2/5

    With only two tools, the server feels thin for a vision-related domain, as it lacks coverage for common operations like image generation, editing, or filtering. The tools are redundant in functionality, making the count seem artificially low for the apparent scope.

    Completeness2/5

    The tool surface is severely incomplete for a vision server; it only offers image analysis via two similar models, with no support for tasks like image creation, transformation, or retrieval. This creates significant gaps that will limit agent capabilities in handling broader vision workflows.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

  • 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?

    With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It mentions 'multimodal capabilities' but doesn't explain what this means operationally, nor does it cover important behavioral aspects like rate limits, authentication requirements, error handling, or what happens when the CLI fails. The description is too vague about the tool's actual behavior.

    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 extremely concise at just one sentence with zero wasted words. It's front-loaded with the core functionality and efficiently communicates the essential purpose without unnecessary elaboration. Every word earns its place in this minimal description.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how to interpret results, error conditions, or operational constraints. The description fails to compensate for the lack of structured metadata, leaving significant gaps in understanding how to effectively use this complex 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?

    With 100% schema description coverage, the schema already documents all 8 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3. The description doesn't explain parameter interactions, default behaviors, or provide examples of effective parameter combinations.

    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 clearly states the action ('describe or analyze an image') and the technology used ('Google Gemini CLI with multimodal capabilities'), which provides a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling 'qwen_analyze_image' beyond mentioning the different technology stack.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives, including its sibling 'qwen_analyze_image'. There's no mention of specific use cases, prerequisites, or comparative advantages that would help an agent choose between available image analysis tools.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description mentions 'multimodal capabilities' but doesn't explain what this entails (e.g., types of analysis, output format, limitations). It also lacks details on permissions, rate limits, error handling, or what happens during execution (e.g., whether it's synchronous). This leaves significant gaps for an agent to understand the tool's behavior.

    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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every element ('Use Qwen CLI', 'describe or analyze an image', 'multimodal capabilities') contributing essential information. There's zero waste in the phrasing.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity (7 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't explain the return values or output format, which is critical since there's no output schema. It also lacks behavioral context (e.g., what 'analyze' entails, error cases, or performance characteristics). For a tool with this many parameters and no structured support, the description should provide more guidance.

    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 input schema fully documents all 7 parameters with clear descriptions. The tool description adds no additional parameter information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.

    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 clearly states the tool's purpose: 'describe or analyze an image with its multimodal capabilities' using Qwen CLI. It specifies the verb ('describe or analyze'), resource ('image'), and technology ('Qwen CLI'), making the purpose unambiguous. However, it doesn't explicitly differentiate from its sibling 'gemini_analyze_image' beyond mentioning Qwen specifically.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'gemini_analyze_image' or any other alternatives, nor does it provide context about when Qwen might be preferred over other image analysis tools. Usage is implied through the tool name and description but not explicitly stated.

    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

mcp-vision-relay MCP server

Copy to your README.md:

Score Badge

mcp-vision-relay MCP server

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/ah-wq/mcp-vision-relay'

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