Loom Advisor

Loom Advisor

Provides tools to list, retrieve, edit, and merge Loom screen recordings.

Category
Visit Server

README

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

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured