YouTube Transcript

Fetch the full transcript of any YouTube video and turn it into whatever the user needs — summaries, blog posts, social content, quotes, show notes, or raw text.

Requirements

• Hyper MCP installed. https://app.hyperfx.ai/mcp
• YouTube toolkit enabled at https://app.hyperfx.ai/integrations — provides youtube_transcript and youtube_reader.

If youtube_transcript is not in the tool list, stop and tell the user to enable the YouTube toolkit in Hyper.

Two tools — pick the right one

Default: start with `youtube_transcript`. Use youtube_reader when you need something the raw text can't give you (e.g. visual descriptions, translation, or a structured extraction from a very long video).

Critical rules

1. `youtube_transcript` takes 15–30 seconds. It spins up an isolated sandbox. Tell the user it's running and to expect a short wait — don't make them think it's stuck.
2. Both video IDs and full URLs are accepted. "NZLAdOL9fP8" and "https://www.youtube.com/watch?v=NZLAdOL9fP8" both work.
3. Don't fabricate transcript content. Always fetch before summarizing. Never rely on training knowledge about what a specific video says.
4. Very long videos (>2 hours): youtube_transcript handles these fine. Only use youtube_reader on long videos if you specifically need AI-powered extraction — it can hit token limits on very long content.
5. No transcript available: Some videos have transcripts disabled. If youtube_transcript fails, try youtube_reader as a fallback — it uses a different extraction method.

Fetching the transcript

youtube_transcript(
    video_id_or_url="https://www.youtube.com/watch?v=NZLAdOL9fP8",
    language="en"   # optional — omit to auto-detect
)

Response structure:

{
  "success": true,
  "video_id": "NZLAdOL9fP8",
  "language": "English (auto-generated)",
  "text": "Full transcript as one string...",
  "segments": [
    { "text": "This week we launched Hyper MCP.", "start": 0.0, "duration": 3.2 },
    { "text": "It brings Hyper's built-in tools...", "start": 3.2, "duration": 4.1 }
  ],
  "total_duration": 342.0
}

Use text for most tasks. Use segments when you need timestamps (e.g. chapters, clip references, karaoke captions).

Using youtube_reader for AI-powered extraction

youtube_reader(
    url="https://www.youtube.com/watch?v=NZLAdOL9fP8",
    instruction="Summarize the key points. Then list the main features demonstrated, with timestamps."
)

Good instruction examples:

• "Extract every claim made about pricing or cost."
• "List the action items mentioned, in order."
• "Translate this to Spanish."
• "What tools or products does the speaker mention by name?"
• "Identify the main sections of this video and give me a timestamp for each."

What to do with the transcript

Once you have the text, ask the user what they need — or infer it from context:

Example outputs

Input: "Get the transcript of https://www.youtube.com/watch?v=NZLAdOL9fP8 and write a LinkedIn post from it"

Flow:

1. Call youtube_transcript(video_id_or_url="https://www.youtube.com/watch?v=NZLAdOL9fP8")
2. Read the returned text
3. Identify the 1–2 sharpest moments — what's surprising, useful, or quotable
4. Draft a LinkedIn post in the speaker's voice (first person) with a hook and a clear point

Input: "Summarize this video for me: [URL]"

Flow:

1. Call youtube_transcript(video_id_or_url="[URL]")
2. Return 4–6 bullet points of key takeaways, without padding or filler

Input: "Pull the timestamps for each section of this video"

Flow:

1. Call youtube_transcript(video_id_or_url="[URL]")
2. Use the segments array to identify topic shifts
3. Return a chapter list: 00:00 — Intro, 01:23 — Feature demo, etc.

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