Use vid2context in LM Studio#

LM Studio can read any video through the vid2context MCP server: the transcript with timestamps, the metadata and the chapters. No key is needed. Captions come straight from the platform, and when a video has none, speech-to-text falls back through free and keyless Whisper rungs.

Install#

These steps use a clone of the repo (the npm package is not published yet, so these steps run the server from source).

1. Open the Program tab in the right sidebar, then Install, Edit mcp.json.

2. Clone the repo and install it once (Node.js 22 or newer). Run the next commands from the repo root.

   shCopy

   git clone https://github.com/nirholas/vid2context.git
   cd vid2context
   npm install

3. Print the config with this clone's paths, then add it to mcp.json. Merge it into mcpServers if the file already has other servers.

   shCopy

   printf '{"mcpServers":{"vid2context":{"command":"%s/node_modules/.bin/tsx","args":["%s/packages/mcp/src/bin.ts"]}}}\n' "$PWD" "$PWD"

Check that it works#

In a chat, the integrations menu lists vid2context. Pick a model that supports tool use.

Try it#

Ask LM Studio:

Get the transcript of https://www.youtube.com/watch?v=jNQXAC9IVRw and summarize it with timestamps.

The get_transcript tool hands the agent a short header and the timed transcript. This is the real text it returns for "Me at the zoo", rendered by vid2context from a recorded run:

Title: Me at the zoo
Channel: jawed
URL: https://www.youtube.com/watch?v=jNQXAC9IVRw
Duration: 0:19
Transcript: creator-captions, en (youtube-captions)
Chapters: 0:00 Intro | 0:05 The cool thing | 0:17 End

[0:01] All right, so here we are, in front of the elephants
[0:05] the cool thing about these guys is that they have really...
[0:07] really really long trunks
[0:12] and that's cool
[0:14] (baaaaaaaaaaahhh!!)
[0:16] and that's pretty much all there is to say

The agent can now cite moments by timestamp. For long videos it can call get_outline first and read only the sections it needs.

Optional keys#

Every key is optional. Each one unlocks more free rungs, which run before the keyless ones:

• GROQ_API_KEY: Groq free tier: fast Whisper speech-to-text and LLMs
• GEMINI_API_KEY: Google AI Studio free tier LLMs
• OPENAI_API_KEY: OpenAI, used last and only when set
• OLLAMA_BASE_URL: Local Ollama server, for example http://localhost:11434/v1
• LOCAL_STT_BASE_URL: Local OpenAI-compatible speech-to-text server, for example http://localhost:8000/v1

The full list, with every model override, is in Providers and rungs.

Links#

• LM Studio MCP documentation
• YouTube transcript MCP server: what the tools do
• Source on GitHub