Skip to main content
Glama
kira4094
by kira4094

glm_5v_understand

Analyze images with GLM-5V-Turbo to convert UI screenshots into code, inspect design mockups, debug visuals, and understand charts or documents. Supports local files and URLs.

Instructions

Analyze an image using GLM-5V-Turbo (Zhipu AI's multimodal vision model). Supports local image files and remote URLs. Excels at: UI screenshot→code, design mockup analysis, visual debugging, chart/document understanding.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesImage source: local file path (e.g. C:/path/to/screenshot.png) or URL (https://...)
detailNoImage detail level. 'high' for fine-grained UI analysisauto
promptYesWhat to ask about the image. Be specific for best results. E.g.: 'Recreate this UI as HTML with Tailwind CSS'
thinkingNoEnable thinking mode for complex reasoning tasks
max_tokensNoMaximum output tokens (max 128K)
temperatureNoSampling temperature (0-2)
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.

Install Server

Other Tools

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/kira4094/glm-vision-mcp-server'

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