Video Convert 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 |
|---|---|
| convert_videoC | 将视频文件转换为指定格式。支持MP4、AVI、MOV、WMV、FLV、MKV、WEBM、M4V等主流格式之间的相互转换。 |
| get_video_infoC | 获取视频文件的详细信息，包括格式、分辨率、时长、编解码器、码率等。 |
| batch_convertB | 批量转换多个视频文件为指定格式。支持同时处理多个文件，提高转换效率。 |
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 3 tools
The tools have some overlap in purpose, particularly between batch_convert and convert_video, which both handle video format conversion. However, the descriptions help clarify that batch_convert is for multiple files while convert_video is for single files, reducing confusion. get_video_info is clearly distinct for metadata retrieval.
All tool names follow a consistent verb_noun pattern with snake_case, such as batch_convert, convert_video, and get_video_info. This predictable naming makes it easy for agents to understand and use the tools without confusion.
With only 3 tools, the count feels thin for a video conversion server, as it lacks operations like update, delete, or more advanced processing. However, it covers basic conversion and info retrieval, making it borderline but not severely mismatched.
There are significant gaps in the tool surface for a video conversion domain. Missing operations include editing videos (e.g., trim, merge), managing conversion jobs (e.g., cancel, list), and handling audio or subtitle tracks, which could lead to agent failures in complex workflows.