Skip to main content
Glama
chiyan11

GLM-4.6V-Flash MCP Server

by chiyan11

analyze_video

Analyze video content from a URL or local file. Provide a prompt or question to receive a detailed description and answers about the video.

Instructions

使用 GLM-4.6V-Flash 分析一段视频(视频需为可访问的 URL 或本地视频文件)。

Args: video: 视频地址,支持 http(s) URL、data URI,或本地视频文件路径。 prompt: 对视频提出的问题或指令。 thinking: 是否开启深度思考模式。 temperature: 采样温度,0~1。 max_tokens: 最大输出 token 数。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoYes
promptNo请详细描述这个视频的内容。
thinkingNo
max_tokensNo
temperatureNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are present, so the description must disclose behavior. It mentions the underlying model (GLM-4.6V-Flash) and input constraints (URL/local file), but does not describe potential side effects, rate limits, failure modes, or output format. 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 compact, with a one-sentence intro followed by a clean list of five parameters. No wasted words.

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?

Given the tool's complexity (5 parameters, one required) and no annotations, the description covers all arguments and the core purpose. It doesn't mention video length/size limits or return values, but an output schema exists, so that gap is partially mitigated. Overall it's a solid description.

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

Parameters5/5

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

The description provides a full 'Args' section that explains each parameter beyond the schema: video supports URL, data URI, or local file; prompt is the question/instruction; thinking is a deep-thinking toggle; temperature is sampling temperature (0-1); max_tokens is the maximum output. This fully compensates for the schema's 0% description coverage.

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: using GLM-4.6V-Flash to analyze a video, with the input requirement of a URL or local file. It explicitly identifies the media type (video), distinguishing it from sibling tools for images and files.

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 context on when to use this tool: whenever video analysis is needed, with supported input formats listed. However, it does not explicitly mention alternatives or when not to use it, though sibling tool names imply that image and file analysis are separate.

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/chiyan11/glm-4.6v-flash-mcp'

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