vidin

vidin

MCP server that converts videos into annotated keyframes and timeline reports for coding agents, with tools to analyze, inspect frames, and zoom into clips.

Category
Visit Server

README

vidin

Turn a video into something a coding agent can actually read.

Claude Code, Codex and friends accept images, not video. So when a bug report arrives as a screen recording, the video is dead weight — someone has to watch it and retype what happened. vidin watches it instead: it converts the recording into a small set of annotated keyframes plus a written timeline, and serves them to agents over MCP.

A 20-second recording condensed to six annotated frames

A 20-second recording in, six frames out: the refund panel, the 500 dialog, the retry toast, the recovery — each boxed and timestamped. (The clip is vidin's synthetic test fixture.)

  • Event-driven, not one-frame-per-second. A frame is kept when the screen changes and then comes to rest. Three minutes of someone reading costs almost nothing; every real UI event is captured.
  • Fast. A 3-minute 1080p recording takes ~4 seconds on an M-series Mac. An hour-long one costs time, not RAM.
  • One command to run. npx @teunlao/vidin — all it asks of the machine is ffmpeg; no sharp, no headless browser, no native image library.
  • Shaped for agent context. The MCP tools return a written report and one contact sheet first; full-detail frames only for the moments that turn out to matter.
  • Timestamps you can scrub to. An agent can say "the error appears at 00:41.2" and you jump straight there in the original file.

Quickstart

brew install ffmpeg        # + yt-dlp, only if you'll pass URLs

Hook it into Claude Code:

claude mcp add vidin -- npx -y @teunlao/vidin mcp

Or in .mcp.json / ~/.codex/config.toml equivalents:

{
  "mcpServers": {
    "vidin": { "command": "npx", "args": ["-y", "@teunlao/vidin", "mcp"] }
  }
}

Or straight from the shell:

npx @teunlao/vidin ~/Desktop/bug.mov       # → ./.vidin/bug/
npm i -g @teunlao/vidin                    # or put `vidin` on PATH for good
vidin https://example.com/screencast.mp4   # downloaded with yt-dlp
vidin zoom bug.mov --from 00:41 --to 00:45 --fps 6
vidin probe bug.mov

What comes out

out/
  REPORT.md        the entry point: timeline table, what changed where, what to open
  manifest.json    the same, machine readable
  sheets/          the whole video as labelled 3x3 grids (~1.3k tokens each)
  frames/          full-detail annotated frames

An annotated frame: red dashed box around the error dialog, timestamp bar

Each frame carries a red dashed box around the regions that changed since the previous kept frame, and a bottom bar with the frame number, the timestamp in the original video, the gap since the previous frame, why the frame was kept, and how much of the screen changed.

The MCP tools

Four tools, shaped so an agent spends context in the right order:

tool what it does
analyze_video video → frames on disk; returns the report text and one contact sheet image
get_frames full-detail frames by number or time range, capped so a call can't flood the context
zoom_clip re-extract a narrow window at a higher frame rate — for the moment two frames don't explain
probe_video duration / resolution / fps / audio, cheap

The intended loop: analyse → look at the sheet → read the timeline → open two or three frames → zoom in if the moment between them is still unclear. Opening everything up front would burn the context before knowing which second matters.

Profiles and tuning

profile for
bug-report default — someone recorded a screen, narrated a bug, stopped
dense short clips where every twitch matters (24 fps analysis, up to 80 frames)
walkthrough long recordings; fewer frames, wider anchors
cheap smallest footprint, for when the context really is tight (12 frames at 1092px)

Tuning without leaving the profile: --max-frames, --sensitivity (percent of the screen that counts as an event, lower = more frames), --fps, --width, --quality, --no-annotate, --no-sheets.

Why pick frames instead of slicing them

One screenshot per second turns three minutes into 180 frames of which ~90% are identical, because the person was reading, thinking or moving the mouse. Those duplicates don't cost much — they bury the handful of frames where something actually happened, and an agent has to look at all of them to find out which. Frame selection is a signal-to-noise problem wearing a cost problem's clothes.

vidin is event driven instead. It walks the video at 12 fps in downscaled RGB and keeps a frame only when something actually happened:

  • Change is measured against the last kept frame, not the previous sample, so slow drift accumulates instead of hiding under the threshold.
  • Frames land after the change, not inside it. When motion starts, nothing is emitted yet — the selector waits for the screen to come to rest and keeps the settled frame. You get the state after the click, not the animation blur.
  • Colour counts as change. Comparison is per channel, not on brightness. A field turning red or a green toast over a grey panel barely moves the luma and is exactly what a bug report is about.
  • A moving mouse costs nothing, by arithmetic rather than special-casing: at analysis resolution a pointer is an order of magnitude below the event threshold. There was once a subsystem for recognising it; measured on real recordings it changed no decision anywhere, so it is gone.
  • Guard rails. A minimum gap, anchor frames across long still stretches, periodic samples while a long animation runs, and a hard budget (40 frames by default) that drops the least informative frames first while keeping the ends of the timeline.

Two invariants hold whatever path a frame took:

  • Nothing is dropped silently. The comparison baseline only ever advances by keeping a frame. There is no path that folds a changed screen into the baseline without emitting it — that would make the change unrecoverable, since every later comparison would measure zero against a state you never see.
  • Annotations describe the neighbour you can actually see. After the frame budget prunes the middle of the timeline, every survivor's change percentage and boxes are recomputed against the frame that now precedes it.

How it works

ffmpeg ─raw RGB @12fps, 400px─ selector ──timestamps+regions── ffmpeg ──annotated WebP
         one decode pass,         event state machine            drawbox/drawtext
         streamed                 in plain TypeScript            per frame

Decoding streams two frames at a time; the selector additionally keeps the downscaled buffers of the frames it has kept so it can recompute their deltas after pruning. That is bounded by the frame budget, not by video length: ~32 MB at the defaults. All drawing is done by ffmpeg filters, which is why there is no image library in package.json.

Development

Development runs on Bun; the runtime code itself sticks to node: APIs, which is what lets the published package run anywhere npx does.

git clone https://github.com/teunlao/vidin && cd vidin && bun install
bun test

The fixture is a synthetic screen recording with a scripted timeline — a panel, an error dialog, a toast, a recovery, and two stretches of pointer-only motion. The suite asserts that each staged event yields exactly one settled frame and that pointer motion alone yields none. test/regressions.test.ts builds its own clips and pins every failure a review has found, so a fixed bug stays fixed.

Not there yet

  • Audio transcript. Narration is the highest-signal part of a bug report. Planned: local Whisper (whisper.cpp or mlx-whisper) → a transcript.md aligned to the frame timeline.
  • OCR over kept frames, so an agent can grep an error message without spending vision tokens at all.
  • Pointer position and clicks are not reported — deliberately. A drawn crosshair was checked against a real recording and was right about half the time: a growing DevTools request list moves down the screen exactly like a pointer does. A wrong marker asserts "the user was pointing here" on a coin flip, and the pointer is plainly visible in the frame anyway.
  • Horizontal scroll is not detected; vertical is.
  • Small elements need a nudge. The default event threshold is 0.4% of the screen, so a progress bar or a badge below that is not an event on its own. --sensitivity 0.2 or --profile dense catches them, at the cost of more frames.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured