Video Fetch MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| download_videoC | 下载指定URL的视频到本地路径 |
| get_video_infoB | 获取视频信息（不下载） |
| get_task_statusC | 获取下载任务状态 |
| cancel_taskC | 取消下载任务 |
| list_tasksB | 列出所有下载任务 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose with no overlap: cancel_task stops tasks, download_video initiates downloads, get_task_status checks progress, get_video_info retrieves metadata, and list_tasks enumerates tasks. An agent can easily distinguish between these operations.
All tools follow a consistent verb_noun pattern (e.g., cancel_task, download_video, get_task_status), using snake_case throughout. The naming is predictable and readable across the entire set.
With 5 tools, this server is well-scoped for video fetching, covering core operations like downloading, status checking, task management, and info retrieval. Each tool earns its place without being excessive or insufficient.
The toolset covers the essential lifecycle of video fetching: initiating downloads (download_video), monitoring (get_task_status, list_tasks), and metadata retrieval (get_video_info). A minor gap is the lack of a tool for modifying or resuming tasks, but agents can work around this with the existing tools.