YouTube Video Summarizer MCP Server
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get-video-info-for-summary-from-urlC | Get details or explanation about a YouTube video, get captions or subtitles of Youtube video from a URL |
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 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as retrieving video details and captions for summarization from a YouTube URL.
Since there is only one tool, naming consistency is inherently perfect. The tool name uses a clear verb-noun pattern (get-video-info-for-summary-from-url) that is descriptive and follows a logical structure.
A single tool is too few for a server named 'YouTube Video Summarizer MCP Server', as it suggests a broader summarization functionality. The tool only fetches video info and captions, lacking operations like generating summaries, processing text, or managing multiple videos, making the scope feel incomplete and thin.
The tool set is severely incomplete for the server's purpose. It only provides video details and captions, but does not include any summarization tools (e.g., generate-summary, analyze-captions) or related operations (e.g., list-videos, save-summary), leaving obvious gaps that will hinder agent workflows.