The visuals of a video —
not just the transcript.
Every other tool hands your AI a transcript and loses the screen — the diagrams, slides, and code where the meaning actually lives. VidLens reads the screen and returns a structured, AI-ready document your agent can call over MCP.
[04:32]“…the request flows from the API gateway through the message queue — as you can see in this diagram.”
Made for the people transcripts leave behind.
The same visual layer, delivered three ways — to your agent, to your notes, and to your channel.
The first MCP server that gives your AI the visuals of a video, not just the transcript.
One line of config and every recording becomes a structured document your agent can call — visual descriptions of what’s on screen, on-screen code, and chapters, all cited to the second.
› get_video_doc(id) ## On screen [04:32] Architecture diagram: Client → API Gateway → Kafka → Worker → PostgreSQL
Notes that show the whiteboard, not just the words.
Formulas, diagrams, and slides captured as real text and linked to the second they appeared — so you can skim the lecture instead of scrubbing through it again.
## Key moments - [12:04] Bayes' rule on the whiteboard <!-- on screen @ 12:04 --> P(A|B) = P(B|A)·P(A) / P(B)
An llms.txt for your channel — make your videos readable by AI search.
Publish clean, structured documents of your own videos at stable URLs, so answer engines and agents can find, read, and cite what you made.
# Maya Chen — video documents - /v/vector-search Vector Search in Production - /v/rag-eval Evaluating RAG, honestly - /v/chunking Chunking is the real model
Your machine reads. Our server writes.
VidLens is split in two on purpose. The reading happens on your own connection; the document is assembled by our hosted server.
Your client reads locally
Point your agent at VidLens. The client runs on your own machine and reads the recording on your connection — never ours.
It sends a small payload
Only the transcript and a few deduplicated key frames travel to VidLens. The recording itself stays with you.
VidLens assembles the document
Our server describes what’s on screen, structures it into chapters and key moments, and returns one clean Markdown document — callable over MCP.
The video never reaches our servers — only the transcript and key frames do. The reading stays on your machine; we assemble the document.
One recording in. A document your AI can read out.
Summary
A two-sentence TL;DR of the whole recording.
Chapters
The recording split into sections, each with a one-line gist and a timestamp.
On-screen visuals
Diagrams, slides, and charts described in language your AI can read and search.
Timestamped transcript
Every line, speaker-labeled, linked to the second it was said.
On-screen text & code
Slides and code captured as markdown — copy-paste ready.
# Vector Search in Production > 18:24 · May 28, 2026 · Maya Chen ## Summary A pragmatic walkthrough of why naive cosine search disappoints at scale, and the three fixes that matter. ## On screen [04:32] Architecture diagram: Client → API Gateway → Kafka → Worker → PostgreSQL. ## Transcript **[03:58] Maya Chen:** If you take one line home: chunk on structure, not count. ```python def chunk(doc): for s in doc.sections: yield s.heading + s.body ```
Your videos, as callable context.
VidLens is an MCP server. Point your agent at it and every recording becomes a structured, AI-ready document it can read directly — visuals included, cited to the second.
See the MCP integrationGive your AI the whole video.
Not just the transcript — the diagrams, slides, and code on screen, as one AI-ready document your agent can call.