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ANnianExplorer

glm-vision-mcp-server

by ANnianExplorer
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of tool confusion. The tool's purpose is clearly defined for image understanding.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern, indicating predictability, though with only one tool the pattern is not fully established.

    Tool Count3/5

    The server has exactly one tool, which feels thin even for a narrow vision domain. The tool itself is comprehensive, but the overall surface is minimal. This is borderline, so score 3.

    Completeness5/5

    The tool covers image description, multi-image comparison, and Q&A, which are the core capabilities expected of an image understanding server. No obvious gaps within its intended scope.

  • Average 4.1/5 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
    • 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 Apache 2.0.

  • 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?

    With no annotations provided, the description carries the burden of disclosing behavior. It reveals the underlying model (glm-4.6v-flash), input types (local path or URL), single-image vs. multi-image distinct behaviors, and language of answers (Chinese). This goes beyond basic operation and provides useful context, though it doesn't mention failure modes or side effects.

    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, well-structured sentence that uses semicolons to separate key behaviors. It front-loads the main purpose, includes necessary details (model, input types, single/multi behavior, question) without any fluff or repetition. Every clause adds useful information.

    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?

    The description is sufficiently complete for an image understanding tool with only two parameters. It covers input formats, single vs. multiple handling, and optional question support. Since an output schema exists, return values are structured elsewhere. It lacks details on known limitations or edge cases, but overall provides a solid contextual picture.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It explains that 'images' can be local file paths or HTTP(s) URLs, and 'question' is an optional specific question to be answered in Chinese, which adds meaning beyond the raw schema. It also clarifies the behavior of images (single vs. multiple) that relates to parameter usage.

    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 uses a vision model to understand one or more images, with specific behavior for single vs. multiple images. It names the resource (images) and the action (understand/describe). No sibling tools exist, so no differentiation is needed, but the verb 'understand' is slightly generic, though detailed description is mentioned.

    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 provides clear context on when to use the tool: for understanding images via local paths or URLs, and optionally asking a specific question. It doesn't explicitly state exclusions or alternatives, but since no sibling tools are listed, this is acceptable. The behavior for single vs. multiple images is also a form of usage guidance.

    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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