videoworker MCP
Allows AI agents to run remote FFmpeg video, audio, and image processing jobs using standard FFmpeg arguments, uploading local media and downloading processed results.
This server has a remotely hosted endpoint. Open its connector listing for connection details, authentication requirements, and setup instructions:
videoworker MCP
Give your AI agent FFmpeg: upload local media, run a remote job, and save the result.
An MIT-licensed, local stdio MCP connector for the videoworker video, audio and image processing API. Bring standard FFmpeg arguments; our workers run them. No local FFmpeg installation required.
Get started free · Hosted MCP · REST API · Releases
Choose your connection
Local connector — this repo | Hosted MCP | |
Best for | Local file uploads and downloads | Connecting without installing Python |
Transport | stdio | Streamable HTTP |
Sign in | Scoped API key in a private file | Browser login and OAuth consent |
Local file access | Named media in your selected folders | None; upload via web or REST first |
Setup | Quick start below |
Both use the same account and allowance. The connector is free software; hosted processing follows the current plans and limits. Free accounts receive a small monthly allowance. Check pricing for the Starter launch offer and its availability; a trial is not required to use Free.
Related MCP server: mcp-video-reader
Quick start
1. Install — no checkout or Git needed
Requires Python 3.12 or newer. Install the versioned release wheel:
python -m pip install "https://github.com/AzadKurt/videoworker-mcp/releases/download/v0.1.0/videoworker_mcp-0.1.0-py3-none-any.whl"
python -m videoworker_mcp --versionOn Windows, use py -3.12 instead of python if needed. On macOS/Linux, your
command may be python3. Use that same Python installation in your MCP config.
Print its full path with:
python -c "import sys; print(sys.executable)"Alternative with Git installed:
python -m pip install "git+https://github.com/AzadKurt/videoworker-mcp.git@v0.1.0"This package is not on PyPI yet. pip install videoworker-mcp is not the command
for this release.
2. Create your workspace and private key file
Sign in to your workspace. Under API keys,
create a key with usage:read, files:write, jobs:read, jobs:write and
outputs:read for all eight tools. Save it as plain text in a file accessible
only to your user. On macOS/Linux: chmod 600 /absolute/path/to/key. On Windows,
keep it in your user profile and restrict its file permissions to you.
Create uploads and downloads folders. Both must exist and use absolute paths. Keep only authorized media in uploads; keep the key file elsewhere. Never paste the key into a chat or a shared client config.
3. Add your client config
Replace the Python and file/folder paths with your own. Merge the entry into an existing config, preserving other servers. The values are paths, not secrets. Restart or reload your client after saving.
Codex
Add to ~/.codex/config.toml:
[mcp_servers.videoworker]
command = "/absolute/path/to/python"
args = ["-m", "videoworker_mcp"]
startup_timeout_sec = 30
tool_timeout_sec = 960
[mcp_servers.videoworker.env]
VIDEO_WORKER_API_KEY_FILE = "/absolute/path/to/videoworker.key"
VIDEO_WORKER_UPLOAD_ROOT = "/absolute/path/to/uploads"
VIDEO_WORKER_DOWNLOAD_ROOT = "/absolute/path/to/downloads"Windows forward-slash paths work, e.g. C:/Users/YOU/media/uploads. Ready-to-edit:
Windows / macOS and Linux.
Keep the hosted connection named videoworker_remote if you also use it.
Claude Desktop
Open Settings → Developer → Edit Config and merge:
{
"mcpServers": {
"videoworker": {
"command": "/absolute/path/to/python",
"args": ["-m", "videoworker_mcp"],
"env": {
"VIDEO_WORKER_API_KEY_FILE": "/absolute/path/to/videoworker.key",
"VIDEO_WORKER_UPLOAD_ROOT": "/absolute/path/to/uploads",
"VIDEO_WORKER_DOWNLOAD_ROOT": "/absolute/path/to/downloads"
}
}
}
}Ready-to-edit: Windows / macOS and Linux.
Cursor
Use the same JSON structure in ~/.cursor/mcp.json (global), or
.cursor/mcp.json (project). Enable the server in Cursor's MCP settings.
Ready-to-edit: Windows /
macOS and Linux.
Examples follow documented stdio config formats. Real MCP SDK discovery is tested; this does not claim every desktop app/version was manually tested.
4. Try a small job
Put a small clip.mp4 in uploads and ask your agent:
Check my videoworker allowance first. I authorize uploading clip.mp4 from my configured uploads folder. Trim it to its first second and compress it as MP4. Create one job with a 10-second runtime limit. Keep its request_id for any retry, wait at least one second between status checks, and save the successful result as clip-small.mp4 in my downloads folder. Do not delete anything.
Flow: get_usage → upload_file → create_job → get_job → download_result.
See example job arguments: replace the input
file ID and generate a new UUID for each intended job.
Tools
Tool | Purpose | API-key scope |
| Allowance, reservations and storage |
|
| Recent owned jobs |
|
| Upload authorized local media |
|
| FFmpeg job with a stable retry UUID |
|
| Job state and output metadata |
|
| Save output to a new local file |
|
| Request cancellation; confirm termination |
|
| Delete results when requested |
|
Configuration and troubleshooting
Environment variable | Value |
| Absolute path to a private plaintext key file |
| Existing absolute folder of authorized media |
| Existing absolute destination folder |
| Optional; defaults to |
VIDEO_WORKER_API_KEY is supported in private process environments and takes
precedence over the file. Never put it in a shared config or command line.
Custom origins require HTTPS, except explicit loopback HTTP for development.
ENOENT / server not found: use the full Python path printed above, not a shell alias. GUI applications can have a different PATH.
Configuration error: set the same variables in a terminal and run
python -m videoworker_mcp --check. It validates local setup only, without API calls or credential/allowance verification. Help/version need no key.Unauthorized / forbidden: check your key, scopes and account in the dashboard. Never send credentials in a support message.
File rejected: pass a flat name, not a path or URL. Local extensions: mp4, mp3, wav, webm, gif, png, jpg, jpeg, mkv, mov, ogg, flac. Linked folders and symlinks/junctions are rejected.
Slow transfer: uploads/downloads can take up to 15 minutes per network operation. Configure suitable tool-call timeouts where supported. Closing a tool call does not cancel an accepted processing job.
Data and retries
The service enforces account ownership, allowance, file limits and expiry. The local process runs as your OS user; folder restrictions are application guards, not an operating-system sandbox. No local FFmpeg is bundled or needed.
Downloads never overwrite files or follow agent-supplied output URLs. Declared output sizes are bounded to 1 GiB; legacy metadata without a size has a 50-MiB fallback cap. Transfers stream instead of loading a full video into memory.
Keep one UUID request_id and identical arguments for job retries (48 hours).
An uncertain response can still mean success. Uploads are not idempotent. Poll
no faster than once per second and download before expiry. Cancellation/deletion
are complete only after server confirmation. No billing/admin permissions needed.
Development
python -m pip install '.[dev]'
python -m pytest -q
python -m ruff check .
python -m ruff format --check .Tests use tiny controlled HTTP fixtures and actual MCP SDK discovery. See release checks, dependency licenses and security reporting.
License and support
MIT for the connector; hosted processing and the private backend are separate. GitHub issues or info@videoworker.dev.
Config references: Codex, Claude Desktop, Cursor.
This server cannot be deployed
Maintenance
Related MCP Connectors
FFmpeg as a service for AI agents: typed video editing tools, async jobs, downloadable outputs.
Run FFmpeg and FFprobe in the cloud: convert, compress, trim and analyze video and audio.
Video, audio, and image processing for AI agents: convert, transcribe, upscale - 150+ operations.
Convert, probe, and split video into frames — real FFmpeg, paid per job with x402 on Base.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables cloud-based FFmpeg video and audio processing through the Rendi API, allowing AI assistants to convert, edit, and manipulate media files without local FFmpeg installation.2MIT
- AlicenseAqualityDmaintenanceProvides video manipulation via ffmpeg (frame extraction, format conversion, trimming, audio adjustments, filters, and subtitle handling) without requiring an external API key.20MIT
- AlicenseAqualityBmaintenanceEnables AI agents to create, monitor, and download video transcodes through the FFmpeg Micro REST API.11182 npm1MIT
- AlicenseAqualityAmaintenanceDeterministic Model Context Protocol (MCP) server for FFmpeg. Gives AI agents 17 structured tools with hardware acceleration for video conversion, compression, waveforms, subtitles, and inspection.17173 npm2MIT