Skip to main content

MCP server for Loom video management

Project description

Loom Advisor

MCP server for Loom video management. Provides tools to list, retrieve, edit, and merge Loom screen recordings.

Features

  • list_recorded_videos - Retrieve a list of recorded videos with pagination and folder filtering
  • get_video - Get detailed information about a specific video
  • edit_video - Edit videos by trimming or extracting clips
  • merge_videos - Combine multiple videos into one

Installation

pip install m2ai-mcp-loom-advisor

Configuration

Required environment variables:

Variable Description
LOOM_ACCESS_TOKEN OAuth2 access token for Loom API

Optional environment variables:

Variable Description Default
LOOM_BASE_URL Loom API base URL https://api.loom.com/v1

Getting an Access Token

Loom uses OAuth2 for authentication. To obtain an access token:

  1. Register your application in the Loom Developer Portal
  2. Implement the OAuth2 authorization flow
  3. Use the returned access token in your configuration

Note: Loom's public API access may be limited. Enterprise users may have additional API capabilities. Contact Loom for API access details.

Usage with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "loom": {
      "command": "loom-advisor",
      "env": {
        "LOOM_ACCESS_TOKEN": "your-access-token"
      }
    }
  }
}

Or run directly with Python:

{
  "mcpServers": {
    "loom": {
      "command": "python",
      "args": ["-m", "loom_mcp.server"],
      "env": {
        "LOOM_ACCESS_TOKEN": "your-access-token"
      }
    }
  }
}

Tools

list_recorded_videos

Retrieve a list of recorded videos from Loom.

Parameters:

  • limit (optional): Maximum number of videos to return (1-100, default 50)
  • offset (optional): Pagination offset (default 0)
  • folder_id (optional): Filter videos by folder ID

Example:

{
  "limit": 10,
  "offset": 0,
  "folder_id": "folder-abc"
}

get_video

Retrieve detailed information about a specific video.

Parameters:

  • video_id (required): Unique identifier for the video

Example:

{
  "video_id": "abc123"
}

edit_video

Edit a video by adding clips or trimming sections.

Parameters:

  • video_id (required): Unique identifier for the video
  • editing_details (required): Object containing edit instructions
    • trim_start: Start time in seconds to trim from beginning
    • trim_end: End time in seconds where video should end
    • clips: List of clip objects with start and end times
    • title: Optional new title for the edited video
    • description: Optional new description

Example:

{
  "video_id": "abc123",
  "editing_details": {
    "trim_start": 5,
    "trim_end": 120,
    "title": "Edited Demo"
  }
}

merge_videos

Combine multiple videos into one merged video.

Parameters:

  • video_ids (required): List of video IDs to merge (minimum 2, in order)
  • title (optional): Title for the merged video

Example:

{
  "video_ids": ["video-1", "video-2", "video-3"],
  "title": "Combined Demo"
}

Development

Running Tests

# Activate virtual environment
source venv/bin/activate

# Run tests
pytest

# Run with coverage
pytest --cov=loom_mcp --cov-report=term-missing

Code Quality

# Format and lint
ruff check src tests
ruff format src tests

# Type checking
mypy src

Project Structure

loom-mcp/
├── src/
│   └── loom_mcp/
│       ├── __init__.py
│       ├── server.py          # MCP server entry point
│       ├── clients/
│       │   ├── __init__.py
│       │   └── loom.py        # Loom API client
│       └── tools/
│           ├── __init__.py
│           ├── list_recorded_videos.py
│           ├── get_video.py
│           ├── edit_video.py
│           └── merge_videos.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_loom_client.py
│   ├── test_tools.py
│   └── test_server.py
├── pyproject.toml
├── README.md
└── .env.example

License

MIT


Generated by GRIMLOCK MCP Factory

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

m2ai_mcp_loom_advisor-1.0.0.tar.gz (14.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

m2ai_mcp_loom_advisor-1.0.0-py3-none-any.whl (15.0 kB view details)

Uploaded Python 3

File details

Details for the file m2ai_mcp_loom_advisor-1.0.0.tar.gz.

File metadata

  • Download URL: m2ai_mcp_loom_advisor-1.0.0.tar.gz
  • Upload date:
  • Size: 14.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for m2ai_mcp_loom_advisor-1.0.0.tar.gz
Algorithm Hash digest
SHA256 33425b329fb447e8a6aa428fbe2658a4304cff14686abac0b16692f36030ab53
MD5 279014f1c3dcf6731999341ebdb5c01b
BLAKE2b-256 e710a52d31204439b6c6e6d005de961a45253e30cffc439862e0de251b58caf5

See more details on using hashes here.

File details

Details for the file m2ai_mcp_loom_advisor-1.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for m2ai_mcp_loom_advisor-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 78d757d6afa1c8de65f483a7aa155d4aac760efe81b74418222972fc1d9e62dc
MD5 4b3ab469f2628dd98f57c206e81157cb
BLAKE2b-256 f3098e5f9e7a1a0af79506e2acc978b44f44ffbe3f2f3901e5b9af47b0ceebdb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page