VideoNote MCP

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Video links → multi-format notes

★ 61 Stars Category · Other Popular

53FMRS · D

VideoNote-MCP is an open-source project (MIT licensed) maintained by HuangYincan that packages the end-to-end "video link → multi-format notes" pipeline as an MCP Server plus a Claude Code Skill; it is not an official product of any upstream platform. By default the Agent in the current conversation writes the notes, with a configured LLM serving only as a fallback when the Agent cannot view images; tools can be decoupled and used on demand, with no backend service required. It suits Claude Code users who need note workflows combining transcription, visual understanding, and danmaku/comments.

Reliability

8/20

Security and permissions

9/20

Maintenance

11/20

Documentation

14/20

Setup experience

11/20

Read the FMRS scoring method →

VideoNote-MCP packages the entire "video link → multi-format notes" pipeline as an MCP Server plus a Claude Code Skill: give the agent a link and it automatically runs download → speech transcription → visual understanding → danmaku/comments, with notes written by the Agent in the current conversation by default (a configured LLM is only a fallback when the Agent cannot view images). It can be used end-to-end (one link → one note) or decoupled, taking generation, material, task, and media-processing tools as needed; no backend service needs to be started. It supports mainstream platforms (e.g. Bilibili, Douyin, YouTube) and can export Markdown, subtitles (srt / vtt / json), and LaTeX mathnote PDF among other formats.

Tools

prepare_note_material

Produce material from one link (download, transcription, visual understanding, danmaku/comments) for the current Agent to write notes from.

generate_note

Fallback generation path: uses the configured LLM when the Agent cannot view images.

process_media

Media processing tool, e.g. action="merge" to merge segmented recordings, action="diarize" for speaker separation.

list_tasks

List all tasks (global index, with semantic titles).

cleanup

Clean up per task (pass task_id) or globally (factory reset, no argument).

health_check

Check FFmpeg / database / whisper readiness.

Setup

1) Install via the plugin marketplace: claude plugin marketplace add HuangYincan/VideoNote-MCP, then claude plugin install videonote@videonote (the MCP launch command uvx videonote@latest automatically pulls the latest version from PyPI). 2) During installation Claude Code prompts for defaults item by item (style/transcription engine/video understanding/comments, etc.); after installing, run /videonote-setup in the session to finish configuration. 3) (Optional) Configure the fallback LLM key, platform QR-code login (Bilibili, Douyin, etc.), or run the CLI wizard with ! videonote setup; the default path does not require an LLM. 4) Restart the session and tell the agent "make notes for this video" with a link.

Fit and risk

Best for

Individual users who want structured, multi-format notes from a single video link quickly
Users working in Claude Code who are willing to have the current conversation Agent write notes directly
Note workflows that combine speech transcription, visual understanding, danmaku, and comments from multiple sources
Users who need task management, multi-video parallelism, and batch output

Not for

Users whose clients do not support MCP / Claude Code Skill (the source only describes a Claude Code Skill and MCP Server form)
Users who do not want to install local dependencies such as FFmpeg and whisper or download videos
Scenarios needing a hosted backend service rather than local execution (this project requires no backend service to start)

Required permissions

Read video links and download the corresponding media locally into task directories (note_results/{task_id}/raw/)
Write generated artifacts locally (transcripts, notes, screenshots, frames, subtitle exports, etc.) into note_results/{task_id}/gen/
Read and write the local SQLite database video_note.db (video_tasks global task index)
Invoke local components such as FFmpeg and whisper
Optional platform QR-code login (Bilibili, Douyin, etc.) and optional fallback LLM API key configuration

Risks and side effects

Downloads video, cover, and other media files, consuming local disk space (cleanup keeps configuration and models by default)
whisper transcription runs locally, so speed and resource use depend on machine performance
Optionally enabling the fallback LLM requires providing an API key, creating key-management risk
Platform QR-code login stores session state, involving account-credential risk
Cleanup operations may delete task data; check first with cleanup(task_id, dry_run=True)

Troubleshooting

Run health_check to verify FFmpeg / database / whisper readiness
Confirm FFmpeg is installed locally, or media processing and transcription will fail
If transcription fails, check that whisper is available and the model has been downloaded
If tasks are missing or titles mismatch, use list_tasks to enumerate the global index (including semantic titles)
Before cleaning up, run cleanup(task_id, dry_run=True) to confirm the impact
If installation does not take effect, restart the session and run /videonote-setup to complete configuration

Use cases

Study and exam prep: end-to-end plus video understanding plus subtitle-based refinement to fully cover a course

Meeting minutes: merge segmented recordings → diarize for speaker separation → meeting_minutes style

Lecture close reading: generate end-to-end, then have the agent refine from the full transcript and fill in details by chapter

Video appreciation: enable danmaku plus comment integration so notes include an "audience views" section

Media processing only: use process_media alone for merging, speaker separation, etc.

Supported clients

Claude CodeFull support

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