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kira4094

glm-vision-mcp-server

by kira4094
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with other tools. The tool's purpose is clearly defined and unambiguous.

    Naming Consistency5/5

    The single tool name follows a clear pattern: model identifier (glm_5v) plus action verb (understand). Since there are no other tools, naming consistency is trivially perfect.

    Tool Count3/5

    With just one tool, the server feels minimal. While it serves a focused purpose, the count is on the low end of reasonable and could benefit from additional related capabilities.

    Completeness5/5

    For a vision-understanding server, the provided tool covers a broad range of tasks including UI analysis, chart understanding, and document processing. No obvious gaps exist for the stated domain.

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

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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?

    With no annotations, the description carries the full burden of behavioral disclosure. It usefully states supported input types ('local image files and remote URLs'), which is beyond what the schema states. However, it does not mention output format, limitations, or side effects. 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 three sentences, front-loaded with the core purpose, and includes a succinct list of use cases. Every sentence earns its place with no fluff or repetition.

    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 vision-analysis tool with no output schema, the description covers purpose, supported inputs, and primary use cases. It doesn't describe return format, but the 'analyze' verb implies textual output. Given no annotations, the description is fairly complete, though it could mention what the tool returns.

    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 input schema provides 100% parameter coverage with detailed descriptions, setting a baseline of 3. The description adds context for the image parameter (local/remote) and prompt examples, but does not significantly exceed the schema's existing documentation. It adds marginal value.

    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 and specifically states the tool's function: 'Analyze an image using GLM-5V-Turbo'. It also lists concrete use cases (UI screenshot→code, design mockup analysis, visual debugging, chart/document understanding), making the purpose unambiguous even without sibling comparisons.

    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 'Excels at' section provides clear guidance on when to use the tool—for UI screenshots, design mockups, visual debugging, and chart/document understanding. While it doesn't explicitly mention alternatives or exclusions, the use-case focus effectively implies suitable scenarios. No siblings exist to compare against.

    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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