Skip to main content
Glama

通用图像理解

image_analysis

Answer questions about any image by providing a local path, URL, or clipboard content. Supports custom detail levels and region specification.

Instructions

通用兜底:理解任意图片并回答问题。不确定用哪个专用工具,或只是想问一张图时使用。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes图片:本地路径 / file:// / http(s):// / data: URI / 'clipboard'(读系统剪贴板,文本宿主推荐)/ 'latest'(VISION_DROP_DIR 里最新图)
regionNo可选:手动指定关注区域,命名如 'top-right' 或归一化 bbox 'x,y,w,h'(0~1)
questionNo具体问题或额外要求
thinkingNo是否开启视觉模型深度推理(默认按工具/后端策略)
detail_levelNo细节级别:overview=单次快速;normal/fine/auto 触发由粗到细的自动缩放(auto 为默认,足够清晰则早退)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
roundsYes实际经历的视觉调用轮数
regionsNo缩放走过的区域轨迹(归一化 bbox)
markdownYes人类可读的结构化 markdown 正文(与 content 一致)
providerYes
warningsYes降级/截断/不确定等告警
confidenceNo模型对结果的置信度
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 disclosing behavioral traits. However, the description only states a high-level purpose without detailing safety, limitations, privacy considerations, or what happens with different image types. This lack of behavioral context is inadequate for a general-purpose 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?

The description is concise at two sentences and front-loaded with the core purpose. It is efficient but could be slightly more structured with a brief list of capabilities or examples. No waste, but room for minor improvement.

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 tool has 5 parameters and a complex sibling set, the description is minimal. It lacks information about output format, error handling, or expected behavior. While the output schema exists, the description should provide a high-level summary of what the tool returns and how to interpret results, which is missing.

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 has 100% description coverage for all 5 parameters, so the schema already documents parameter meanings clearly. The description adds no additional semantic value beyond the schema. With full schema coverage, a baseline score of 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?

The description clearly states the tool's purpose as a general fallback for understanding arbitrary images and answering questions. It explicitly distinguishes from specialized siblings by advising use when unsure which specialized tool to use, making the purpose and scope very clear.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool ('when unsure which specialized tool, or just want to ask about an image'), which implies when not to use it. This directly helps the agent select the appropriate tool among siblings with specialized functions.

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/Pelican0126/vision-mcp'

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