videopilot 0.1.3

Agent-driven video creation toolkit: neural TTS voiceover, highlight cutting, timeline composition, and NLE export.

videopilot

Agent-driven video creation toolkit. An MCP server giving LLMs 20 tools to author voiceover, cut highlights, compose timelines, and render finished MP4s — plus a CLI for manual / scripted runs when you don't want an agent in the loop.

videopilot is an MCP (Model Context Protocol) server that lets a calling LLM — driven by GitHub Copilot CLI or any other MCP-aware client — turn raw screen recordings into narrated, edited MP4s. The server exposes 20 tools covering the full pipeline: neural TTS voiceover, faster-whisper transcription, silence detection, clip cutting, timeline composition with slides and audio ducking, and NLE export to EDL / FCPXML.

The MCP server is the primary interface. A standalone videopilot CLI ships alongside it for manual or scripted runs — useful for one-off stages, CI, or workflows without an agent in the loop.

source.mp4  ->  script.json  ->  tts  ->  cut-plan.json  ->  cut  ->  compose-plan.json  ->  compose  ->  final.mp4
                                                                                                          + EDL / FCPXML / replay script

Highlights

Capability	Engine
Neural voiceover, 400+ voices, 100+ locales	Microsoft Edge TTS (free, no key)
Premium neural voices	Azure Speech (optional, with key)
Word-level transcription	faster-whisper (local)
Silence trimming, scene cuts	ffmpeg
Title slides, picture-in-picture, audio ducking, music underlay	ffmpeg filter graph composer
MP4 render at any resolution / fps	ffmpeg
Hand-off to Premiere / Resolve / Final Cut	EDL (CMX 3600) + FCPXML export
Replayable render scripts	PowerShell / bash export
Agent integration	MCP server with 20 tools — see MCP tools
Authoring contract	JSON state files documented in AGENT.md (incremental authoring, schema introspection, idempotency probes)

Install

From PyPI (recommended)

pip install --user videopilot

Two console scripts are installed:

Script	Purpose
videopilot-mcp	The MCP server (stdio transport). Wire this into your MCP client.
videopilot	The manual CLI. Useful for one-off stages and CI.

Verify the install:

videopilot doctor

You also need ffmpeg on PATH:

OS	Command
Windows	winget install --id Gyan.FFmpeg -e
macOS	brew install ffmpeg
Debian / Ubuntu	sudo apt install ffmpeg
Fedora	sudo dnf install ffmpeg
Arch	sudo pacman -S ffmpeg

videopilot doctor exits 0 when ffmpeg, ffprobe, Python deps, and optional Azure keys are all in order; otherwise it prints exactly what's missing. The same check is also exposed as the doctor MCP tool.

From source (development)

git clone https://github.com/mbahgatTech/videopilot.git
cd videopilot
pip install --user -e .

Via the Agency plugin

If you use Copilot or Claude inside Microsoft and have access to the Agency Playground marketplace, install the videopilot plugin and ask:

set up videopilot

The plugin's init skill runs the same installer logic for you.

Connect to an MCP client

videopilot-mcp runs the MCP server over stdio. The verified config for the GitHub Copilot CLI (~/.copilot/mcp-config.json) is:

{
  "mcpServers": {
    "videopilot": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "videopilot", "videopilot-mcp"],
      "tools": ["*"]
    }
  }
}

uvx pulls the latest videopilot from PyPI into an ephemeral environment and runs the videopilot-mcp entry point — no global install required. If you already have videopilot installed globally (pip install --user videopilot) you can instead use "command": "videopilot-mcp" with no args.

Any MCP-aware client that supports stdio servers can run videopilot-mcp the same way — consult your client's docs for the exact config-file location and schema.

After your client restarts, the agent can call any of the 20 videopilot.* tools below.

MCP tools

Tool	Purpose
doctor	Verify ffmpeg, ffprobe, Python deps, optional Azure keys.
voices	List available neural TTS voices (Edge TTS or Azure).
list_projects	List all projects under projects/.
project_status	Pipeline status for one project: which JSON state files exist, which stages have run.
init	Create a new project, optionally with a first source video.
import_source	Add another source to an existing project.
read_state	Read a JSON state file (project / script / cut-plan / compose-plan).
write_state	Write a JSON state file with schema validation.
tts	Synthesize voiceover MP3s from script.json (async, emits progress notifications).
transcribe	Run faster-whisper; returns word-level segments and writes SRT.
silence	Emit a cut-plan candidate that strips silence.
cut	Cut clips per cut-plan.json.
compose	Render final MP4 per compose-plan.json.
export	Emit NLE projects (EDL, FCPXML) and replayable ffmpeg script.
schema	Return JSON schemas (agent-facing) for every state file.
add_vo_segment	Append or upsert a voiceover segment in script.json.
add_slide	Append a slide entry (with optional body text) to compose-plan.json.
set_compose_output	Set compose output resolution / fps / codec.
preview_slide	Render a single slide as a PNG for fast preview without running compose.
is_up_to_date	Probe whether a stage's outputs are current for its inputs (idempotency check).

The contract — what each tool reads and writes, the JSON state-file schemas, and the recommended call order — is documented in AGENT.md. Calling agents should read AGENT.md before issuing tool calls.

CLI reference (manual mode)

Each pipeline stage is also exposed as a videopilot CLI subcommand. Use it when you want to run a step by hand, drop the agent, or invoke from CI.

Command	Purpose
videopilot doctor	Verify ffmpeg, ffprobe, Python deps, optional Azure keys.
videopilot voices [--locale en-US]	List available TTS voices.
videopilot init <slug> [--source PATH]	Create a new project with optional first source.
videopilot import <slug> <path>	Add another source to an existing project.
videopilot tts <slug> [--force]	Synthesize voiceover MP3s from script.json.
videopilot transcribe <slug> <source-id>	Run faster-whisper; emits word-level JSON + SRT.
videopilot silence <slug> <source-id>	Emit a cut-plan candidate that strips silence.
videopilot cut <slug> [--force] [--reencode]	Cut clips per cut-plan.json.
videopilot compose <slug>	Render final MP4 per compose-plan.json.
videopilot export <slug> [--edl] [--fcpxml] [--script]	Emit NLE projects + replayable ffmpeg script.

Run videopilot <command> --help for per-command flags.

Manual quick start

# 1. Create a project with a source video
videopilot init demo --source "/path/to/raw-recording.mp4"

# 2. Hand-author projects/demo/script.json (one segment per beat of narration),
#    OR have your agent draft it from AGENT.md.

# 3. Synthesize the voiceover
videopilot tts demo

# 4. (Optional) transcribe to help pick highlights
videopilot transcribe demo raw1

# 5. Hand-author projects/demo/cut-plan.json (which spans to keep)

# 6. Cut clips from sources
videopilot cut demo

# 7. Hand-author projects/demo/compose-plan.json (timeline + slides + ducking)

# 8. Render the final video
videopilot compose demo

# 9. Optional: emit NLE projects + replay script
videopilot export demo --edl --fcpxml --script

Final output: projects/demo/out/final.mp4 plus optional final.edl, final.fcpxml, and render.ps1.

Project layout

videopilot/
- AGENT.md           <- contract for calling LLMs (start here if you're driving the tool)
- README.md          <- this file
- LICENSE            <- MIT
- pyproject.toml
- videopilot_mcp.py  <- MCP server (primary entry point; console-script: videopilot-mcp)
- videopilot.py      <- argparse router (CLI implementation)
- videopilot_cli.py  <- console-script shim for the CLI
- lib/               <- shared implementation modules
  - tts.py
  - transcribe.py
  - silence.py
  - cut.py
  - compose.py
  - export.py
  - ffmpeg_wrap.py
  - voices.py
  - init_cmd.py
  - doctor.py
- examples/          <- copyable starter JSON state files
- tests/             <- standalone test scripts (mcp_e2e.py, progress_smoke.py)
- projects/<slug>/   <- per-project workspace (one folder per video)
  - project.json
  - script.json
  - cut-plan.json
  - compose-plan.json
  - sources/
  - voice/
  - transcripts/
  - clips/
  - tmp/
  - out/

Configuration

Environment variable	Purpose
AZURE_SPEECH_KEY	Optional. Enables Azure Speech voices (premium neural TTS).
AZURE_SPEECH_REGION	Required when AZURE_SPEECH_KEY is set (e.g. eastus).

Edge TTS is the default and requires no configuration.

Development

git clone https://github.com/mbahgatTech/videopilot.git
cd videopilot
pip install --user -e ".[dev]"

# Build the package
python -m build

# Validate the dist
python -m twine check dist/*

# Local smoke test
videopilot doctor

# MCP server stdio + progress-notification smoke test
python tests/progress_smoke.py

# End-to-end MCP test (real ffmpeg + Edge TTS + full pipeline)
python tests/mcp_e2e.py

Releasing

Releases publish to PyPI automatically when a v* tag is pushed. The version is derived from the tag itself via setuptools_scm — there is no version = line in pyproject.toml and no version-bump commit is required.

The workflow uses PyPI Trusted Publishing (OIDC), so no API tokens are stored in the repo or in GitHub Secrets — PyPI verifies the GitHub OIDC token at publish time.

One-time setup (PyPI side, done once before the first release):

1. Sign in to https://pypi.org/.
2. Account settings → Publishing → Add a new pending publisher:
   • PyPI project name: videopilot
   • Owner: mbahgatTech
   • Repository: videopilot
   • Workflow filename: release.yml
   • Environment name: pypi
3. On GitHub, repo Settings → Environments → New environment → pypi. Optionally add a required reviewer for an extra approval gate.

Cutting a release:

git tag v0.2.0
git push origin v0.2.0

That's it. The release workflow then:

1. Builds sdist + wheel (version derived from the tag)
2. Verifies the tag matches the setuptools_scm-derived version
3. Runs twine check
4. Publishes to PyPI via OIDC
5. Creates a GitHub Release with the sdist + wheel attached

License

MIT. See LICENSE.