Potencialize seu agente
O comando do próprio publicador. Não é um endpoint do PlugMCP.
Host a copy with our keys and logs.
uvx jevreel==0.2.0O Claude Code não tem link de instalação para um comando local. Copie o comando abaixo e execute-o em um terminal.
TYPESAFE_API_KEY=<secret> claude mcp add io.github.deepaleph-jevreel -- uvx jevreel==0.2.0Execute em um terminal.
{ "mcpServers": { "io.github.deepaleph-jevreel": { "command": "uvx", "args": [ "jevreel==0.2.0" ], "env": { "TYPESAFE_API_KEY": "<secret>" } } }}claude_desktop_config.json
URL remota ou um comando na sua máquina? Como as duas instalações diferem
Rodar no PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
AbrirDo publicador
A zero-embedding approach to video intelligence, using Jev.
jevreel lays a video out on labelled contact sheets, one frame per second and sixty frames to an image, and has , TypeSafe's fast decision model, judge every sheet in parallel. The best windows are re-checked on full frames. Your agent (Claude Code, Codex, Cursor, Claude Desktop, anything that speaks MCP) gets back moments, times and probabilities, then looks closer itself. No vectors are computed or stored anywhere.
Search and understand videos without embeddings: contact sheets plus Jev typed decisions.
Five unedited Claude Code sessions using the jevreel MCP server and skill on Blender's open movie Tears of Steel (12:14, (CC) Blender Foundation, CC BY 3.0). Each clip is drawn from its recording: the agent's tool calls, what Jev answered for every sheet and frame, the hits, the cost, and the agent's own answer, quoted. Click a clip for the full-resolution video.
The reference photo is a crop of the frame at 9:18, so the 9:17 hit is its own source; the other seven are what the search found.
The session starts with two failed calls, left in: a shell command the recording didn't allow, then a wrong folder. The error names the folder relative paths resolve from, and the next call works.
Multimodal embedding models already search video well: EmbeddingGemma 2 (October 2026) puts text, images, video frames and audio in one vector space and runs on a phone. jevreel takes a different route to the same goal, with different trade-offs.
Embeddings are the better fit for querying a large library over and over: once the vectors exist, each search is a lookup that costs nothing, while every jevreel question pays Jev for the minutes it screens (about $0.02–0.03 per hour of video). The two combine well: embeddings to shortlist videos, jevreel to find, verify and describe the moment.
flowchart LR
V[video file or URL] --> F["ffmpeg: 1 frame/s"]
F --> S["contact sheets<br/>60 labelled tiles, 1 minute each"]
V --> T["captions, or local<br/>speech-to-text"]
S --> J{{"screen: one Jev request<br/>per sheet, in parallel"}}
T --> J
J --> R["verify: top-ranked windows<br/>re-checked on full frames,<br/>every 0.5 s around the pick"]
R --> H["hits: time, probability,<br/>frame, nearby speech"]
H --> A["your agent: zoom, locate,<br/>read, cite 12:04"]
T01, T02, and so on. Fetch captions, or transcribe locally. Ask every sheet a fixed
set of questions once (people, on-screen text, cuts, setting, activity, a representative frame)
plus any lenses you add, which gives a typed timeline.zoom re-renders full-resolution frames; locate puts a Box around a target in
one frame and crops it; whole answers one question about the entire video from a storyboard,
the timeline and the transcript.Answers are cached: the same images and questions to the same model never cost twice.
This follows TypeSafe's own patterns. Their line-search cookbook pairs a Choice over labelled units with a Noul for "is it there at all". Their images guide searches a large image by Choice over tiles and looks closer only where needed. Their Box guide gates every box behind a Noul and checks its edges.
You need Python 3.11+, FFmpeg on your PATH, and a TypeSafe API key.
uv tool install "jevreel[all]"
export TYPESAFE_API_KEY=...
jevreel index talk.mp4 # prints the video id
jevreel ask talk "a slide with a bar chart" # id, id prefix, path or title all work
jevreel zoom talk 12:00 12:10 # full-resolution frames to look at
[all] adds URL support (yt-dlp) and local speech-to-text (faster-whisper). Without them, jevreel
indexes local files and uses .srt or .vtt captions saved next to the video.
A lens is a set of typed questions asked of every sheet and kept as timeline columns. Questions can
be noul (yes/no, plus the second it happens), choice (one of your options) or score (levels,
lowest first).
# examples/lenses/driving.toml
name = "driving"
[[questions]]
name = "on_bridge"
type = "noul"
ask = "Is the car driving on a bridge?"
[[questions]]
name = "weather"
type = "choice"
ask = "What is the weather mostly like?"
options = ["clear", "cloudy", "rain", "snow", "fog"]
[[questions]]
name = "traffic"
type = "score"
ask = "How much other traffic is on the road?"
levels = ["None", "Light", "Moderate", "Heavy"]
jevreel index drive.mp4 --lens driving.toml # rides in the indexing requests: almost free
jevreel lens drive driving.toml # or run it later: one request per minute of video
jevreel query drive "on_bridge > 0.7" # filter as often as you like: no requests
jevreel query drive "weather == snow and traffic >= 2"
The MCP server exposes ten tools. The skill teaches agents the workflow: ask, look before claiming anything, cite times.
The plugin bundles the MCP server and the skill. It starts the server with
uvx --from "jevreel[url]==0.2.0", so you need uv and FFmpeg on
your PATH. Local speech-to-text is left out to keep the first start quick; for it, add the server
by hand with jevreel[all] as shown further down.
Claude Code asks for your TypeSafe API key when you enable the plugin and keeps it in your system's credential store:
claude plugin marketplace add DeepAleph/jevreel
claude plugin install jevreel@jevreel
Codex passes plugin servers only an allowlist of environment variables, so save the key once. The same command installs the environment the plugin starts, so its first start is fast:
uvx --from "jevreel[url]==0.2.0" jevreel login
codex plugin marketplace add DeepAleph/jevreel
codex plugin add jevreel@jevreel
Claude Code
claude mcp add jevreel -e TYPESAFE_API_KEY=your-key -- uvx --from "jevreel[all]" jevreel mcp
Codex
codex mcp add jevreel --env TYPESAFE_API_KEY=your-key -- uvx --from "jevreel[all]" jevreel mcp
Claude Desktop, Cursor and other mcpServers clients
{
"mcpServers": {
"jevreel": {
"command": "uvx",
"args": ["--from", "jevreel[all]", "jevreel", "mcp"],
"env": { "TYPESAFE_API_KEY": "your-key" }
}
}
}
VS Code uses the same entry under "servers" with "type": "stdio".
The skill (Claude Code, Codex and other agents that read Agent Skills):
npx skills add DeepAleph/jevreel
Agents without MCP can use the skill with the CLI alone; every command prints JSON.
jevreel measure <video> reports what one contact sheet costs at several grid sizes on your account.
import asyncio
from jevreel import Reel
async def main() -> None:
async with Reel() as reel:
video = await reel.index("talk.mp4")
result = await reel.ask(video.id, "a slide with a bar chart")
for hit in result.hits:
print(hit.time, hit.probability, hit.frame)
print(reel.query(video.id, "text_on_screen > 0.8").rows)
asyncio.run(main())
Measured on 2026-10-15 with jev-1.14.0 at $0.042 per million input tokens, on a 10-minute 1080p video:
One question across an hour of video comes to about $0.02–0.03. Asked at index time, lens questions
ride along with the default ones, which is where the "nearly free" comes from: an 8-question request
cost about the same as a 2-question one. TypeSafe doesn't document image token counts yet. These
figures are what the API reported. Check yours with jevreel measure.
A 10-minute 1080p gameplay capture, no speech, every result checked against the frames by hand:
JEVREEL_VERIFY_TOP, or lower JEVREEL_TILES_PER_SHEET for larger tiles.Issues and pull requests are welcome. See CONTRIBUTING.md. Coding agents should read AGENTS.md.
MIT. jevreel is an independent project, not affiliated with TypeSafe AI. Jev is TypeSafe's model and is used through its public API under TypeSafe's terms.
Search and understand videos without embeddings: contact sheets plus Jev typed decisions.
{ "mcpServers": { "io.github.deepaleph-jevreel": { "command": "uvx", "args": [ "jevreel==0.2.0" ], "env": { "TYPESAFE_API_KEY": "<secret>" } } }}Ferramentas
Ainda não há lista de ferramentas. Servidores remotos recebem uma após a primeira verificação de saúde.
Perguntas frequentes
Copie nesta página a configuração stdio para Cursor, Claude Desktop, Claude Code ou VS Code. Ela usa a instalação do publicador, não um proxy do PlugMCP.
O publicador lista estas variáveis de ambiente: TYPESAFE_API_KEY.
Jevreel roda localmente via stdio.
Última verificação: 2026-11-09. Search and understand videos without embeddings: contact sheets plus Jev typed decisions.