Kinocut

Guardrailed video editing MCP server for AI agents.
Local-first FFmpeg tools, Video Receipts, quality gates, Hyperframes, and Shorts/Reels repurposing — for Claude Code, Cursor, and any MCP client. Free, Apache-2.0. Formerly mcp-video.

Demo • Status • 1.8.0 • Whats next • Install • Quick Start • Tools • Tool Reference • Rescue • AI-video • Agent Skill • kinocut.dev • What is Kinocut? • FAQ • llms.txt

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What is Kinocut?

Kinocut is a free, open-source Model Context Protocol (MCP) server, Python library, and kino CLI that gives AI agents a guardrailed local video-editing surface. It wraps FFmpeg (and optional Hyperframes / Whisper extras) with typed tools, preflight validation, Video Receipt provenance, and quality/release checkpoints so agent-produced media can be inspected before publish.

Also known as	kino (CLI); formerly mcp-video / mcp_video
Latest published release	1.8.0 (2026-07-14)
Product site	kinocut.dev
PyPI	kinocut
MCP Registry	io.github.KyaniteLabs/kinocut
Source	GitHub · Forgejo (canonical)
License	Apache-2.0
Runs on	Your machine (macOS, Linux, Windows) — FFmpeg required on PATH
Not	A hosted cloud editor, credit-metered SaaS, or untyped FFmpeg shell wrapper

Best-fit searches: video editing MCP server · AI agent video editing · FFmpeg MCP · Claude Code video tools · Cursor MCP video · Shorts/Reels automation · local AI video workflow · guardrailed agentic media.

Machine-readable brief for AI crawlers: llms.txt · site: kinocut.dev/llms.txt.

See It Work

Tell the agent what you want in plain language:

"Trim this interview to the strongest 45 seconds, add burned captions, make it vertical, and quality-check it before export."

Kinocut turns that into typed, guardrailed tool calls - no FFmpeg flags to guess, no silently broken exports:

from kinocut import Client
video = Client()

clip = video.trim("interview.mp4", start="00:02:15", duration="00:00:45")
video.ai_transcribe(clip.output_path, output_srt="captions.srt")
captioned = video.subtitles(clip.output_path, subtitle_file="captions.srt")
short = video.resize(captioned.output_path, aspect_ratio="9:16")
video.release_checkpoint(short.output_path)  # thumbnail + quality gate before you publish

Primary job: turn a local interview or podcast into captioned vertical clips with a Video Receipt agents can re-run and humans can approve.

Three things people use it for

• Repurposing — one recording into captioned Shorts, Reels, and TikTok packages with manifests and review artifacts.
• Podcast & interview cuts — find the strongest segment, normalize audio, add chapters, and export.
• Agent-driven media in CI — repeatable, reviewable edits from Claude Code, Cursor, Codex-style clients, or scripts.

Paths: Install matrix · Golden path · Prompts · Tutorial · Compare · When to recommend

Status and releases

Surface	Version / tip	What it means
PyPI / npm / GitHub Release	1.9.0 (2026-07-15)	Latest published package. Install with pip install -U kinocut.
This repository (master)	150 MCP tools / 129 CLI commands	Matches published 1.9.0 at tag (release-artifact policy surfaces: review, publish gate, learning, cost, recipe, capabilities, benchmark).
Next	trusted execution kernel + sound program depth	See Whats next. Not pinned to a specific package version yet.

Install from PyPI for the stable package. Clone master only when you intentionally need post-tag tip work.

What's in 1.9.0 (latest release)

Kinocut 1.9.0 is what you get from pip install kinocut today (150 MCP tools / 129 CLI commands). 1.9.0 adds public MCP/CLI/client surfaces for eight release-artifact policy engines (review package, publish gate, review decisions, learning report, cost ledger, recipe capture, capabilities, benchmark) on top of the 1.8.0 contract-first AI-video foundation:

• Everything from the 1.7.0 identity cutover (kinocut package, kino CLI, MCP Registry id, kinocut.dev)
• Governed AI-video — content-addressed ingest, unified preflight, temporal inspect, exact-asset verdict / acceptance, body-swap, lineage-bound salvage (docs/AI_VIDEO_REVIEW_AND_SALVAGE.md)
• Optional C2PA on path-based export (off by default; verify-after-sign)
• Staged MCPB package foundations (Desktop install experiments; not a fully self-contained published runtime)
• Field safety — loss-proof add-audio policies; authored ASS + dimension-aware subtitles
• Hyperframes under MCP — hyperframes_init no longer hangs without a TTY
• Compatibility: mcp-video==1.6.2 installs kinocut==1.8.0; mcp_video imports, MCP_VIDEO_*, ~/.mcp-video, mcp-video://, and legacy receipt keys remain supported on the 1.8.x line

Also still on the published line from earlier releases:

• Agent workflow engine, video rescue, post-rescue planning, layered compositing, expanded preflight guardrails

Full notes: CHANGELOG.md · v1.8.0 release

Whats next

Post-1.8 product work (not a published package version):

Already in 1.8.0

Area	What landed on master	Start here
Governed AI-video	Content-addressed video_ingest, unified video_preflight, temporal evidence (video_inspect_temporal), exact-asset video_verdict / video_acceptance_eval, audio-preserving video_body_swap, lineage-bound video_salvage	docs/AI_VIDEO_REVIEW_AND_SALVAGE.md
Project store / contracts	Append-only private project storage, strict canonical records, protected-element checks, fail-soft optional visual providers	docs/AI_VIDEO_CONTRACTS.md · docs/AI_VIDEO_INSPECTION.md
Field safety	Loss-proof add-audio duration policies; authored ASS + dimension-aware SRT/VTT subtitles	CHANGELOG.md 1.8.0
C2PA provenance	Optional signing on path-based export / Client.export() via c2patool (off by default; only reports signed after verify)	docs/C2PA_PROVENANCE.md
MCPB packaging	Staged Desktop package + fail-closed native builder foundation; not a published self-contained runtime yet	docs/MCPB.md
Repurpose skill	Path-based skills/kinocut-repurpose + deterministic current-tools demo (marketing seed, not the final kernel-backed product)	docs/REPURPOSE_SKILL.md
Hyperframes under MCP	hyperframes_init no longer hangs without a TTY (non-interactive init + closed stdin)	CHANGELOG.md 1.8.0

Upcoming pipeline (in progress)

Two coordinated programs remain after the published 1.8.0 release:

1. AI-video + review/salvage finish

Contract-first media identity → inspection → human-gated verdict → bounded derivatives. Remaining work includes independent Wave-3 verification freeze, audio continuity, subtitle/graphics QA depth, asset intelligence, editorial planning, learning reports, and whole-program acceptance. Sequencing: wishlist parallel execution · current status: post-1.8 program status.

2. kinocut_sound (Sonic World) — full-episode audio production

Standalone-capable sound package inside this repo (kinocut_sound/): plan/timeline/routing/consent → voice → post/spatial → ambience/world → mix/stems → QA/metadata → thin public adapters → host joins → dual-class benchmark → STOP.

Slice	Focus	Status (as of 2026-07-14)
S1–S4	Contracts, authorization, registry/policy, script/episode planning	Implemented foundation leaves
S5 / S7 / S8	Base voice, post/spatial chain, ambience/world	Integrated leaves on master
S6 / S10	Consent-gated clone/blend; voice consistency	Integrated leaves on master
S9 / S11	Mix assembly/stems; QA + metadata	Integrated leaves on master
S12	Thin public discovery / Python adapters	Integrated (capability discovery surface)
S13	Kinocut/host joins (D41/D42 production bindings)	Blocked — external owner receipts incomplete
S14	Dual-class benchmark (Apple silicon + x86 Linux)	Partial — x86 available; Apple class external_host_unavailable
S15	Adversarial acceptance + release STOP	STOP — no ship without dual-class S14, S13 receipts, independent review, human authorization

Authoritative receipts: sound program handoff · S13–S15 gate · sound plan index.

3. Trusted execution kernel (post-program, gated)

The approved trusted execution layer plan still defines the durable product path after the current program: durable edit projects, async render/resume wrapping video_workflow_*, receipt lineage, then kernel-backed repurposing as the “made just by prompting” moment. The protected-timeline kernel does not start merely because sound/AI-video leaves land — it needs the named upstream contract and an explicit human gate.

Product checklist: ROADMAP.md.

Agent Workflow Engine

Agents can plan, validate, render, recover, and prove a multi-step local video job from a single JSON job-spec — through MCP (video_workflow_*), the CLI (workflow-*), or the Python client (Client.workflow_*) — with receipts strong enough for another agent or a human to trust before and after a render. Ops are a small allowlist (probe | trim | resize | convert | merge | add_text | composite_layers) mapped 1:1 to the same vetted engine functions the individual tools use; media references are symbolic and workspace-confined; everything fails closed.

{
  "schema_version": 1,
  "name": "captioned-vertical-short",
  "sources": { "hero": { "path": "input/hero.mp4" } },
  "steps": [
    { "id": "trim-hero", "op": "trim", "inputs": { "src": "@sources.hero" },
      "params": { "start": 0, "duration": 6 }, "output": "@work/hero_trim.mp4" },
    { "id": "vertical", "op": "resize", "inputs": { "src": "@work/hero_trim.mp4" },
      "params": { "width": 1080, "height": 1920 }, "output": "@work/hero_vertical.mp4" },
    { "id": "caption", "op": "add_text", "inputs": { "src": "@work/hero_vertical.mp4" },
      "params": { "text": "Watch this", "position": "bottom-center" }, "output": "@outputs.master" }
  ],
  "outputs": { "master": { "path": "output/final.mp4" } }
}

kino workflow-validate --spec job.json    # cheap structural gate, no render
kino workflow-plan     --spec job.json --save-plan plan.json     # dry-run op graph + hashes
kino workflow-render   --spec job.json --save-receipt receipt.json   # execute + provenance receipt
kino workflow-inspect  --receipt receipt.json    # read-only integrity re-check

The render receipt records per-step input/output hashes, a resume cursor, and a cleanup manifest, all with workspace-relative paths:

{
  "receipt_kind": "workflow",
  "versions": { "mcp_video": "1.8.0", "ffmpeg": "8.1" },
  "spec_hash": "sha256:be2f3a9b...",
  "steps": [
    { "id": "trim-hero", "op": "trim", "status": "completed",
      "input_hashes": { "src": "sha256:3b976d49..." },
      "output": "work/be2f3a9b-2effedb3/mcp_video_hero_trim.mp4", "output_hash": "sha256:00727499..." },
    { "id": "caption", "op": "add_text", "status": "completed",
      "output": "output/final.mp4", "output_hash": "sha256:8633ad2a..." }
  ],
  "cleanup_manifest": { "cleaned": true, "policy": "clean-on-success" },
  "resume_cursor": { "last_completed_step": "caption", "next_step": null },
  "status": "completed",
  "render_determinism_scope": "spec/input/output hashes are deterministic; rendered bytes may vary across FFmpeg builds"
}

--all-variants emits N distinct outputs from one declaration, and --resume continues a job that failed with its intermediates kept (fail-closed on a changed spec). Full schema, @ref grammar, variants, resume, and cleanup are in docs/WORKFLOWS.md; a runnable spec is in examples/workflows/.

Governed AI-video review

On the development tip, Kinocut adds a contract-first path for agent-edited media that must stay attributable and reviewable:

1. Ingest the source into a private content-addressed project (video_ingest / video-ingest)
2. Preflight + temporal inspection on the stored asset (video_preflight, video_inspect_temporal)
3. Verdict + acceptance with exact human evidence (video_verdict, video_acceptance_eval)
4. Bounded derivatives only — audio-preserving body swap or allowlisted salvage recipes (video_body_swap, video_salvage), each with lineage and a fresh non-approved review slot

There is no force/bypass flag. Analyzer output alone cannot approve. Stale, aliased, or protected inputs fail closed. Operating guide: docs/AI_VIDEO_REVIEW_AND_SALVAGE.md. These surfaces ship in published 1.8.0 — see Status and releases.

Dedicated Video Rescue

For "fix this clip" requests where the story and timeline must remain unchanged, use the review-first rescue pipeline. Plan and inspect the diagnosis, approve only safe repair IDs, render, then inspect the verified package. The source stays immutable; master and universal sharing copy are always verified; optional captions remain sidecars. See docs/RESCUE.md for CLI, MCP, Python, cancellation, resume, and stable errors.

Layered Compositing

composite-layers / video_composite_layers adds a spec-driven ordered layer stack for agents that need more than two-shot overlay primitives. It supports image, video, and solid layers; normal alpha compositing; per-layer opacity; x/y placement; transform sizing; timing windows; and mask/matte alpha sources — plus allowlisted full-canvas and positioned blend modes (multiply, screen, overlay, darken, lighten) and rotation with a new pivot reference point. Dry-run plans and deterministic layer_plan v2 receipts capture source, filtergraph, and output hashes.

kino composite-layers --spec layers.json --dry-run --save-layer-plan layer-plan.json
kino composite-layers --spec layers.json -o out.mp4 --save-layer-plan layer-plan.json

Use composite-layers when an agent needs a planned stack of overlays, mattes, lower thirds, blurback plates, or platform variants that should be reviewed before rendering. A non-normal blend layer may remain full-canvas, or it may use the positioned allowlist: explicit width and height, an integral nonnegative in-canvas position, full opacity, and no scale, rotation/pivot, mask/matte, or timing window. Positioned blend crops the running base to the rectangle, blends the same-size layer, then overlays the result back at that position. Other blend geometry fails closed with unsupported_blend_geometry; output is video-only.

Public Discovery

Kinocut is built to be findable and citable by both search engines and AI answer engines:

• Canonical product URL: https://kinocut.dev/
• GitHub README + llms.txt with entity facts, install commands, and safety rules
• Official MCP Registry record under io.github.KyaniteLabs/kinocut
• FAQ answers in this README and docs/faq.md (answer-first, versioned claims)

Kinocut vs raw FFmpeg (and vs cloud editors)

	Kinocut	Raw FFmpeg in agent shell	Typical cloud editor API
Interface	Typed MCP / Python / CLI	Free-form flags	Hosted HTTP API
Preflight	Guardrails before render	Agent invents flags	Vendor-specific
Provenance	Video Receipts + hashes	Ad-hoc logs	Vendor dashboard
Media location	Local-first	Local	Upload required
Core cost	Free (Apache-2.0)	Free	Often metered

Why It Exists

AI agents can write FFmpeg commands, but they should not have to guess flags, parse brittle stderr, or silently publish broken media. Kinocut gives agents typed operations, inspectable tool metadata, structured results, preflight guardrails, and quality checkpoints so a video workflow can be automated and reviewed without turning into shell-command roulette.

Use it when you want an AI assistant to:

• trim, merge, resize, crop, rotate, transcode, or export video;
• add text, subtitles, watermarks, overlays, filters, fades, effects, and transitions;
• extract audio, normalize audio, synthesize audio, add generated audio, or create waveforms;
• detect scenes, make thumbnails, generate storyboards, compare quality, and create release checkpoints;
• scaffold cinematic projects, read STYLE_/NEG_ blocks, parse storyboard tables, and expand shot prompts;
• create new Hyperframes projects, inspect rendered layouts, capture websites, generate local speech, remove backgrounds, and post-process the result with FFmpeg tools;
• repurpose one source video into vertical, horizontal, and square local delivery packages with manifests and review artifacts;
• drive repeatable media workflows from Claude Code, Cursor, Codex-style clients, scripts, or CI.

Installation

Prerequisite: FFmpeg must be installed and available on PATH.

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

Run without a global install:

uvx --from kinocut kino doctor

Or install with pip:

pip install kinocut
kino doctor

For Claude Desktop-style MCPB installs, Kinocut includes a staged local package at mcpb/ and a local build script:

python3 scripts/build-mcpb.py

This package is honest about its runtime: it launches an existing Python environment with Kinocut installed and still requires local FFmpeg. Native self-contained bundles remain blocked pending FFmpeg provenance, licensing, and clean-machine gates. See docs/MCPB.md.

Optional C2PA signing for final MP4 exports is available on the development tip when c2patool and a manifest/signer are configured. Signing is off by default and only reports signed after a verification read succeeds. See docs/C2PA_PROVENANCE.md.

Hyperframes tools additionally need Node.js 22+ and a resolvable Hyperframes CLI. Install/pin Hyperframes in the active Node package layout, add hyperframes to PATH, or set MCP_VIDEO_HYPERFRAMES_COMMAND.

Which extra do I need?

The core install covers all FFmpeg editing tools. Optional features ship as extras — install only what you use:

You want	Install	Approx. extra size
Speech-to-text subtitles (Whisper)	pip install "kinocut[transcribe]"	~1 GB (torch)
Image analysis (colors, layout, contrast)	pip install "kinocut[image]"	~50 MB
Vocal/instrument stem separation	pip install "kinocut[stems]"	~2 GB (torch + demucs)
AI upscaling	pip install "kinocut[upscale]"	~2 GB (Python ≤3.12)
Procedural audio/music tools	pip install "kinocut[audio]"	~30 MB (numpy)
Everything AI	pip install "kinocut[ai]"	several GB

Mix freely, e.g. pip install "kinocut[transcribe,image]". Run kino doctor afterward — it reports exactly which features are available and what is missing.

Upgrading from mcp-video

Kinocut preserves the original surface during the rename window. Existing installs can upgrade without changing code:

pip install --upgrade mcp-video
mcp-video doctor

mcp-video==1.6.2 is a metadata-only compatibility installer for kinocut==1.8.0. The mcp_video import, mcp-video command, MCP_VIDEO_* environment variables, ~/.mcp-video data directory, mcp-video:// resource URIs, and existing receipt keys remain supported on the 1.8.x line. New integrations should use kinocut, from kinocut import Client, and the kino command.

En español

Kinocut es un servidor MCP de edición de video para agentes de IA. La última versión publicada es 1.8.0 (pip install kinocut) con 142 herramientas MCP y 121 comandos CLI sobre FFmpeg para recortar, unir, subtitular, mezclar audio, aplicar efectos y reutilizar contenido (Shorts, Reels, TikTok), más un motor de flujos de trabajo (workflow) con recibos verificables, rescate de video, revisión AI-video gobernada y barreras de seguridad antes de renderizar.

Requisito: FFmpeg instalado y disponible en el PATH.

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg

# Instalación y diagnóstico
pip install kinocut
kino doctor

Para Claude Code:

claude mcp add kinocut -- uvx --from kinocut kino

kino doctor informa qué funciones están disponibles y qué falta instalar. La documentación completa está en inglés; los mensajes de error principales son bilingües.

Quick Start

Golden path (60 seconds)

Prove the install works before wiring an agent host:

pip install -e .          # or: pip install kinocut
kino doctor               # required checks must pass
python scripts/golden_path.py

Success criteria and failure recovery: docs/GOLDEN_PATH.md.
Shareable pack (receipt + quality + media): python scripts/generate_golden_pack.py → demo/golden-pack/.

Try the receipt-backed proof first

From a clone of this repo, run the smallest confidence workflow before wiring an agent host:

uv run --no-project --with kinocut python workflows/05-confidence-baseline/workflow.py
uv run --no-project --with kinocut python workflows/benchmarks/run_confidence_benchmark.py

The workflow generates a tiny source clip, creates a checked vertical video, runs quality/release checkpoint steps, and writes workflows/05-confidence-baseline/output/video_receipt.json.

Proof notes live in docs/proofs/. Public marketing claims (version, tool counts, URLs) live in docs/public_claims.json and are CI-guarded.

Claude Code

claude mcp add kinocut -- uvx --from kinocut kino

Claude Desktop

{
  "mcpServers": {
    "kinocut": {
      "command": "uvx",
      "args": ["--from", "kinocut", "kino"]
    }
  }
}

Cursor

{
  "mcpServers": {
    "kinocut": {
      "command": "uvx",
      "args": ["--from", "kinocut", "kino"]
    }
  }
}

Then ask your agent:

Trim this interview into a 45-second vertical clip, add burned captions, normalize the audio, make a thumbnail, and create a release checkpoint before export.

Agent Skill

Kinocut includes a public agent skill at skills/kinocut/SKILL.md. Use $kinocut in compatible agent hosts when you want the agent to choose between the MCP server, CLI, and Python client while preserving the inspect, edit, verify, and human-review workflow.

For path-based short-form packages from current tools only (no invented commands, no external publish), see skills/kinocut-repurpose/SKILL.md. That skill is an explicit marketing seed; the durable kernel-backed repurposing product is still on the trusted-execution roadmap.

Python Client

from kinocut import Client

editor = Client()

clip = editor.trim("interview.mp4", start="00:02:15", duration="00:00:45")
caption_file = "captions.srt"
editor.ai_transcribe(clip.output_path, output_srt=caption_file)
captioned = editor.subtitles(clip.output_path, subtitle_file=caption_file)
vertical = editor.resize(captioned.output_path, aspect_ratio="9:16")
checkpoint = editor.release_checkpoint(vertical.output_path)

print(checkpoint["thumbnail"])
print(checkpoint["storyboard"])

CLI

kino info interview.mp4
kino trim interview.mp4 -s 00:02:15 -d 45
kino video-ai-transcribe clip.mp4 --output captions.srt
kino subtitles clip.mp4 captions.srt
kino resize clip.mp4 --aspect-ratio 9:16
kino video-quality-check clip.mp4
kino repurpose clip.mp4 --platforms youtube-shorts instagram-reel tiktok

What Agents Can Do

Workflow	Example prompt
Social clips	"Turn this landscape recording into a captioned TikTok and YouTube Short."
Podcast production	"Find the strongest segment, trim it, normalize audio, add chapters, and export."
Product demos	"Create a short launch video from screenshots, title cards, and voiceover."
Cinematic planning	"Create a style pack and storyboard, then render shot prompts for generation."
Quality review	"Compare these two exports, make thumbnails, and flag visual or audio problems."
Batch automation	"Convert this folder of clips to web-ready MP4 with consistent loudness."
Code-created video	"Scaffold a Hyperframes composition, inspect it, render it, then add subtitles and a watermark."
Local repurposing	"Turn this master clip into Shorts, Reels, TikTok, and YouTube assets with thumbnails and a manifest."
Video rescue	"Diagnose this damaged clip, propose only safe repairs, render an approved package, and verify the receipt."
Governed review (dev tip)	"Ingest this export into a project, run preflight and temporal inspection, write a verdict, and salvage only the broken region."

MCP Tools

Published 1.8.0 registers 142 MCP tools and 121 CLI commands (including governed AI-video surfaces). The table summarizes core categories — search_tools discovers the exact operation without loading every description.

Category	Count	Highlights
Core video editing	32	trim, merge, resize, crop, rotate, convert, overlays, subtitles, export, cleanup, templates, merge-compatibility guardrails
Project-backed inspection	3	content-addressed ingest, unified preflight, temporal evidence packages
Governed AI-video	4	exact-asset verdicts, acceptance evaluation, audio-preserving body swaps, lineage-bound salvage
Agent workflow engine	4	validate, plan, render, resume, inspect multi-step jobs with provenance receipts
Dedicated rescue	3	diagnose, approve, render, verify, quarantine, and resume local content-preserving repairs
Post-rescue planning	8	semantic timelines/query, EDLs, visual transforms, restoration, composition, autopilot, explicit egress
Cinematic creation	4	project scaffold, style-pack parsing, storyboard parsing, shot prompt expansion
AI-assisted media	11	transcription, scene detection, upscaling, stem separation, silence removal, color grading
Hyperframes	18	init, preview, render, snapshots, inspect, catalog, website capture, local TTS, transcription, background removal, diagnostics, benchmark, post-process
Repurposing	2	dry-run manifests, platform-ready variants, thumbnails, storyboards, release checkpoints
Procedural audio	7	synthesize, compose, presets, effects, sequences, generated audio, spatial audio, mix-parameter guardrails
Visual effects	8	vignette, glow, noise, scanlines, chromatic aberration, luma key, mask, shape mask, bounded filter parameters
Transitions	3	glitch, morph, pixelate
Layout and motion	6	grid, picture-in-picture, split-screen, animated text, counters, progress bars, auto-chapters, layout mismatch warnings
Analysis	8	scene detection, thumbnail, preview, storyboard, quality compare, metadata, waveform, release checkpoint
Image analysis	3	extract colors, generate palettes, analyze product images
Discovery	1	search_tools

from kinocut import Client

editor = Client()
matches = editor.search_tools("subtitle")
print(matches["tools"])

Full reference: docs/TOOLS.md

Agent-Safe Workflow

For autonomous agents, the intended path is inspect, edit, verify, then ask a human to review release artifacts:

from kinocut import Client

client = Client()

print(client.inspect("trim"))

result = client.pipeline(
    [
        {"op": "trim", "input": "source.mp4", "start": "00:01:00", "duration": "00:00:45"},
        {"op": "add_text", "text": "Launch clip", "position": "top-center"},
        {"op": "normalize_audio"},
        {"op": "resize", "aspect_ratio": "9:16"},
        {"op": "export", "quality": "high"},
        {"op": "release_checkpoint"},
    ],
    output_path="final-short.mp4",
)

Safety contract:

• Media-producing calls return structured results with output paths.
• High-risk edit paths now run preflight guardrails before FFmpeg execution: filter bounds, merge compatibility, audio mix volume/timing, overlay/watermark/chroma opacity and similarity, animated text timing/overflow, and grid/split-screen mismatch warnings.
• Analysis and discovery calls return structured JSON reports.
• Tool discovery is available through search_tools() and Client.inspect().
• Unexpected keyword errors are converted into actionable MCPVideoError guidance.
• Do not publish agent-generated video without video_quality_check, video_release_checkpoint, and human visual/audio inspection.
• For governed AI-video derivatives (dev tip), require stored identities, active human decision evidence, and a fresh review slot after every salvage or body-swap — never raw FFmpeg workarounds labeled as governed.

FAQ

What is Kinocut?

Kinocut is a free, open-source MCP server, Python library, and kino CLI for AI-agent video editing. It wraps FFmpeg (and optional Hyperframes/Whisper extras) with preflight guardrails, Video Receipts, and quality checkpoints. It was formerly named mcp-video.

How do I install it?

brew install ffmpeg   # or apt install ffmpeg
pip install kinocut
kino doctor
claude mcp add kinocut -- uvx --from kinocut kino

Is it free and local-first?

Yes. Apache-2.0, runs on your machine, no Kinocut account or API key required for the core surface, and media is not uploaded to a Kinocut cloud.

Which agents work with it?

Any MCP-compatible client that can run a local stdio server (Claude Code, Cursor, Windsurf, Cline, and similar). You can also use the Python client or CLI without an agent.

How many tools are there?

Published 1.8.0 documents 142 MCP tools / 121 CLI commands. Historical 1.7.0 cutover was 135 / 114.

Was it called mcp-video?

Yes. mcp-video==1.6.2 installs kinocut==1.8.0. Compatibility imports, CLI name, env vars, data dir, resource URIs, and receipt keys remain supported on the 1.8.x line.

More answers: docs/faq.md · on-site FAQ: kinocut.dev/#faq

Documentation

• Documentation map
• Product site
• Tool reference
• Python client reference
• CLI reference
• Agent workflow engine
• Video receipts
• Video rescue
• Post-rescue planning
• AI-video review and salvage
• AI-video contracts
• AI-video inspection
• C2PA provenance
• MCPB packaging
• Product roadmap
• Trusted execution layer plan
• Wishlist / 1.8 program parallel plan
• Sound program status
• AI agent discovery guide
• FAQ
• Golden path (first-run proof)
• Public claims (version / counts)
• Golden demo pack
• Changelog
• llms.txt

Testing

Development verification lives in docs/TESTING.md. Keep public-surface, media workflow, and security checks current when changing tool behavior.

Development

git clone https://git.kyanitelabs.tech/KyaniteLabs/kinocut.git
cd kinocut
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v -m "not slow and not hyperframes"

Community

• Contributing
• Code of Conduct
• Governance
• Maintainers
• Security
• Support
• Roadmap
• Changelog
• Forgejo issues

License

Apache 2.0. See LICENSE.

Built with FFmpeg, Hyperframes, and the Model Context Protocol.

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Part of KyaniteLabs

More from KyaniteLabs. Related projects:

• Epoch — time-estimation MCP server (PERT) for AI agents
• DialectOS — Spanish dialect localization MCP server & CLI
• checkyourself — local-first production-readiness checks for AI-built code

→ More at kyanitelabs.tech

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If Kinocut is useful to you, star or watch it — it helps other agent builders find it.

Built by Simon Gonzalez De Cruz — available for Forward-Deployed / Applied-AI engineering and contract work via the public profile links above.