mcp-video

Guardrailed video editing MCP server for AI agents.
Structured tools for FFmpeg video editing, cinematic prompt planning, media analysis, subtitles, audio, effects, Hyperframes video creation, local repurposing packages, and preflight validation that helps prevent silent bad media output.

Demo • Install • Quick Start • Agent Workflows • Tools • Tool Reference • Video Rescue • AI Discovery • Agent Skill • llms.txt • MCP Registry

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."

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

from mcp_video 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

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.

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) 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" } }
}

mcp-video workflow-validate --spec job.json    # cheap structural gate, no render
mcp-video workflow-plan     --spec job.json --save-plan plan.json     # dry-run op graph + hashes
mcp-video workflow-render   --spec job.json --save-receipt receipt.json   # execute + provenance receipt
mcp-video 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": "1.6.0", "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/.

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 full-canvas 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.

mcp-video composite-layers --spec layers.json --dry-run --save-layer-plan layer-plan.json
mcp-video 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 must be full-canvas (position {0,0}, full opacity, no scale/mask/timing) or it fails closed; output is video-only. Positioned/scaled/masked/timed blend, rotation + mask, and per-layer effect routing are tracked as later phases so this surface stays deterministic and preflightable.

Public Discovery

mcp-video is a free, open-source Model Context Protocol (MCP) server, Python library, and CLI that gives AI agents a real video-editing surface. It wraps FFmpeg, PUSHING CREATION-style planning, media analysis, quality checks, subtitles, audio generation, effects, Hyperframes rendering, local repurposing packages, and guardrails for risky edit parameters behind structured tool schemas.

Best-fit searches:

• video editing MCP server
• AI agent video editing
• FFmpeg MCP tools
• Claude Code video editing
• Cursor MCP video tools
• Python video editing library
• subtitle automation
• reels and shorts automation
• agentic media pipeline
• local AI video workflow
• Hyperframes video creation
• YouTube Shorts repurposing

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. mcp-video 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 mcp-video mcp-video doctor

Or install with pip:

pip install mcp-video
mcp-video doctor

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:

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

En español

mcp-video es un servidor MCP de edición de video para agentes de IA: 135 herramientas estructuradas sobre FFmpeg para recortar, unir, subtitular, mezclar audio, aplicar efectos y reutilizar contenido (Shorts, Reels, TikTok), más un motor de flujos de trabajo (workflow) que planifica, valida, renderiza, reanuda y prueba trabajos de varios pasos con recibos verificables, y barreras de seguridad que detectan parámetros riesgosos 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 mcp-video
mcp-video doctor

Para Claude Code:

claude mcp add mcp-video -- uvx --from mcp-video mcp-video

mcp-video 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

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 mcp-video python workflows/05-confidence-baseline/workflow.py
uv run --no-project --with mcp-video 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/.

Claude Code

claude mcp add mcp-video -- uvx --from mcp-video mcp-video

Claude Desktop

{
  "mcpServers": {
    "mcp-video": {
      "command": "uvx",
      "args": ["--from", "mcp-video", "mcp-video"]
    }
  }
}

Cursor

{
  "mcpServers": {
    "mcp-video": {
      "command": "uvx",
      "args": ["--from", "mcp-video", "mcp-video"]
    }
  }
}

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

mcp-video includes a public agent skill at skills/mcp-video/SKILL.md. Use $mcp-video 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.

Python Client

from mcp_video 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

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

What Agents Can Do

MCP Tools

mcp-video currently registers 135 MCP tools. The table below summarizes the documented core categories; search_tools lets agents discover the exact operation they need without loading every tool description into context.

from mcp_video 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 mcp_video 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.

Documentation

• Tool reference
• Python client reference
• Post-rescue planning
• CLI reference
• AI agent discovery guide
• FAQ
• 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/mcp-video.git
cd mcp-video
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.

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

If mcp-video 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.