yt-analysis-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Your Gemini API key from Google AI Studio |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| summarize_videoA | Summarize a YouTube video's content. Returns a text summary based on the specified detail level. |
| ask_about_videoB | Ask a specific question about a YouTube video's content. Returns an answer based on the video. |
| extract_screenshotsA | Extract key screenshots from a YouTube video at important moments. Uses AI to identify visually significant timestamps, then extracts frames. Returns both base64 images and optionally saves to disk. |
| get_video_timestampsA | Preview mode: Use AI to identify important moments in a YouTube video and return their timestamps WITHOUT extracting frames. Use this to preview what timestamps would be selected before committing to extraction. |
| extract_framesA | Extract frames from a YouTube video at specific timestamps you provide. Use this when you already know the exact timestamps you want (e.g., from get_video_timestamps or video summary). |
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 ambiguity: ask_about_video answers questions about content, extract_frames extracts frames at user-provided timestamps, extract_screenshots uses AI to find and extract key frames, get_video_timestamps previews AI-identified timestamps, and summarize_video provides text summaries. The descriptions explicitly differentiate their use cases, such as distinguishing between extract_frames (manual timestamps) and extract_screenshots (AI-selected timestamps).
All tool names follow a consistent verb_noun pattern with underscores, using clear action verbs like ask_about, extract, get, and summarize paired with descriptive nouns like video, frames, screenshots, and timestamps. There are no deviations in style or convention, making the set predictable and easy to understand at a glance.
With 5 tools, the count is well-scoped for the YouTube analysis domain, covering core workflows like content querying, frame extraction (both manual and AI-driven), timestamp previewing, and summarization. Each tool earns its place without redundancy, and the number is manageable for agents to navigate effectively.
The tool set provides strong coverage for video analysis, including content understanding, visual extraction, and summarization, with no dead ends. A minor gap exists in lacking direct video metadata retrieval (e.g., title, duration, uploader), but agents can work around this by using existing tools like ask_about_video or summarize_video for such information.