# 1000ri-jp/atsurae [Health: Active]

**Category:** 🎥 Multimedia Process  
**Repository:** https://github.com/1000ri-jp/atsurae  
**GitHub Stars:** 2  
**Views:** 5  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/1000ri-jp-atsurae

## Description
AI-powered video editing MCP server with 10 tools for timeline editing, 5-layer compositing, semantic operations, and FFmpeg rendering (1920x1080, 30fps H.264+AAC).

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):

```json
"mcpServers": {
  "atsurae": {
    "command": "uvx",
    "args": ["atsurae"],
    "env": {
      "ATSURAE_API_URL": "",
      "ATSURAE_API_KEY": ""
    }
  }
}
```

**Requires environment variables:** `ATSURAE_API_URL`, `ATSURAE_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What 1000ri-jp/atsurae MCP server does

The 1000ri-jp/atsurae MCP server gives an AI agent a structured interface for editing video projects. Its tools cover clip-level timeline work, audio-track management, higher-level timeline operations, visual inspection, quality checks, render control, and rollback. The project describes a five-layer compositing model: background on layer 1, screen captures or slides on layer 2, an avatar on layer 3, effects on layer 4, and text overlays on layer 5.

The server is intended for conversational workflows in which an agent first inspects a project, applies edits, checks the result, and starts a render. The documented output is an MP4 using 1920x1080 resolution, 30fps, H.264 video, and AAC audio.

## How it works

The local MCP process acts as the connection between an MCP client and the atsurae.ai service. Tool calls are sent through the MCP protocol to the service's REST API, which performs video-engine operations and FFmpeg rendering. The architecture therefore depends on both the local Python package and access to the configured atsurae.ai API endpoint.

A typical workflow can use `atsurae_inspect` to view a project's summary, structure, or full timeline, followed by `atsurae_edit` for clip changes and `atsurae_audio` for volume, ducking, background music, or narration. `atsurae_semantic` handles operations such as closing gaps, snapping clips, and reordering them. `atsurae_batch` can submit up to 20 operations atomically. Preview and analysis tools provide frames, event points, diffs, and checks for gaps, pacing, and composition before rendering.

## Setup and configuration

Install the Python package with pip or uv, then run the module locally:

```bash
pip install atsurae
python -m atsurae
```

The 1000ri-jp/atsurae MCP server uses two environment variables in the documented Claude Desktop configuration:

- `ATSURAE_API_URL`: the atsurae.ai API base URL, shown as `https://api.atsurae.ai`.
- `ATSURAE_API_KEY`: the API key used to access that service.

Claude Desktop connects to the local process with the `python` command and the `-m atsurae` arguments. After adding the MCP entry to `claude_desktop_config.json`, restart Claude Desktop before using the tools.

## Tools and capabilities

The available tools are:

- `atsurae_inspect`: inspect project state at summary, structural, or full-timeline detail.
- `atsurae_edit`: add, move, trim, transform, and delete timeline clips.
- `atsurae_audio`: manage track volume, ducking, BGM, and narration.
- `atsurae_semantic`: perform higher-level timeline operations.
- `atsurae_batch`: execute up to 20 operations as one atomic call.
- `atsurae_preview`: retrieve preview frames, event points, and before/after differences.
- `atsurae_analyze`: check for gaps, pacing issues, and composition problems.
- `atsurae_render`: start, monitor, and download renders.
- `atsurae_history`: review operations and roll back changes.
- `atsurae_pipeline`: run the end-to-end AI video creation pipeline.

## Limitations and notes

The 1000ri-jp/atsurae MCP server requires an atsurae.ai API URL and API key; the README does not describe an offline-only mode or local rendering configuration. Its documented render format is fixed at 1920x1080, 30fps, H.264 plus AAC in an MP4 container. The public REST API documentation is identified as coming soon, so API-specific behavior beyond the listed MCP tools is not established by the provided material. The repository is MIT licensed.

_Full upstream README: https://allmcps.com/mcp/1000ri-jp-atsurae/readme_