youtube-to-docs: Instructions file for Claude Code

YouTube to Docs Plugin

This plugin provides an MCP server to process YouTube videos using the youtube-to-docs library.

Tools

process_video

Processes a YouTube video to generate transcripts, summaries, Q&A, and infographics.

Here are the following args for the tool:

• url: The YouTube URL, video ID, playlist ID, or comma-separated list of IDs.
• output_file: (Optional) Path to save the output CSV. Defaults to youtube-to-docs-artifacts/youtube-docs.csv. Can be a local path, workspace (or w) for Google Drive, sharepoint (or s) for Microsoft SharePoint, hf for a Hugging Face dataset (requires hugging_face_dataset and the HF_TOKEN env var), memory (or m) to keep artifacts in memory (no files on disk), or none (or n) to skip saving to a file.
• hugging_face_dataset: (Optional) The Hugging Face dataset repository to store artifacts in when output_file is hf. Can be a full repo id (e.g. username/my-dataset) or a plain name (e.g. Code for America Summit 2026 Recap) which is slugified and namespaced under the authenticated user. If omitted and the input is a playlist, the playlist's title is used as the dataset name. Requires the HF_TOKEN environment variable.
• transcript_source: (Optional) Source for the transcript. Defaults to 'youtube' (fetches existing). set to an AI model name (e.g., 'gemini-3.5-transcribe', 'gemini-3.5-flash-lite', 'gcp-chirp3') to perform STT on extracted audio. gemini-3.5-transcribe is Google's dedicated STT model and returns word-level timestamps plus speaker diarization, so its SRT has real offsets and [Speaker N] labels on speaker changes. For gcp- models, YTD_GCS_BUCKET_NAME env var is recommended.
• model: (Optional) The LLM model(s) to use for summarization, Q&A, speaker extraction and tag generation (e.g., 'gemini-3.5-flash-lite'). Can be a comma-separated list.
• tts_model: (Optional) The TTS model and voice to use (e.g., 'gemini-3.1-flash-tts-preview-Kore', 'gemini-3.1-flash-tts-preview-Kore', 'gcp-chirp3', 'gcp-chirp3-Kore', 'aws-polly', 'aws-polly-Ruth').
• infographic_model: (Optional) The image model to use for generating an infographic (e.g., 'gemini-3.5-flash-lite-image' or 'gemini-3-pro-image').
• alt_text_model: (Optional) The LLM model to use for generating multimodal alt text for the infographic. Defaults to the summary model.
• no_youtube_summary: (Optional) If True, skips generating a secondary summary from the YouTube transcript when using an AI model for the primary transcript.
• translate: (Optional) Translate all outputs to a target language. Format: {model}-{language} (e.g., 'gemini-3.5-flash-lite-es', 'bedrock-nova-2-lite-v1-fr', 'aws-translate-es', 'gcp-translate-es'). Use aws-translate to use the AWS Translate service directly, or gcp-translate to use Google Cloud Translation API directly. Defaults to English only.
• combine_infographic_audio: (Optional) If True, combines the infographic and audio summary into a video file (MP4). Requires tts_model and infographic_model.
• suggest_corrected_captions: (Optional) Suggest WCAG 2.1 Level AA / Section 508 corrected captions using an LLM. Format: {model} or {model}-{source} (e.g., 'gemini-3.5-flash-lite', 'gemini-3.5-flash-lite-youtube', 'gemini-3.5-flash-lite-gcp-chirp3'). Source can be youtube or an STT model name used for transcription; omit source to auto-detect the most recent AI-generated SRT. Output saved to suggested-corrected-caption-files/.
• post_process: (Optional) Post-process the transcript with JSON operations. Example: '{"word count": "apple"}' counts occurrences of 'apple' in the transcript. Values can be a list for multiple words: '{"word count": ["apple", "banana"]}'. Results are added as columns in the output CSV.
• all_suite: (Optional) Shortcut to use a specific model suite for everything (e.g. 'gemini-flash', 'gemini-pro', 'gemini-flash-pro-image', 'gcp-pro').
• verbose: (Optional) If True, enables verbose output.