Log inSubmit server
Atsurae logo

Atsurae

by 1000ri-jp

AI agents can edit timelines, composite layers, manage audio, analyze projects, and render MP4 video through MCP.

InstallRepositoryWebsite
Full README Compare alternatives Embed widget Claim this listing

Install Atsurae

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "1000ri-jp-atsurae": {
      "command": "uvx",
      "args": [
        "atsurae"
      ],
      "env": {
        "ATSURAE_API_URL": "YOUR_VALUE_HERE",
        "ATSURAE_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

The install command didn't complete successfully in our automated test.

uvx atsurae

error: No solution found when resolving tool dependencies cause: Because atsurae was not found in the package registry and you require atsurae, we can conclude that your requirements are unsatisfiable.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

The 1000ri-jp/atsurae MCP server exposes AI video-editing operations through MCP and forwards them to the atsurae.ai REST API. It provides 10 tools for timeline changes, five-layer compositing, audio control, semantic edits, previews, quality analysis, history, and FFmpeg-based rendering. Reach for it when an MCP-compatible agent needs to create or revise structured video projects through natural-language instructions.

Use cases

  • Edit clips on a video timeline
  • Compose videos across five layers
  • Adjust music, narration, and ducking
  • Analyze pacing and composition issues
  • Render and download finished MP4 videos

Key features

  • 10 MCP tools for video editing
  • Five-layer compositing model
  • Atomic batches of up to 20 operations
  • Timeline inspection and rollback
  • Quality analysis and visual previews
  • FFmpeg MP4 rendering

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 24d ago

Recent health check succeeded.

15/15 checks healthy over the last 88d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10/5/2026 via OSV.dev · atsurae (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars2Stargazers on the source repository.
Last commit8mo agoMost recent push to the default branch.
Install checkInconclusiveInstall command did not finish in our automated test.
Directory activity6 viewsConfig copies, upvotes, and views on AllMCPs.

How Atsurae works

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:

Terminal
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

Its documented render format is fixed at 1920x1080, 30fps, H.264 plus AAC in an MP4 container. The repository is MIT licensed.

Read the full README View source on GitHub

Reviews

No reviews yet — be the first to share how it worked for you.

Frequently Asked Questions about Atsurae

Install the `atsurae` Python package with `pip install atsurae` or `uv pip install atsurae`, then run it with `python -m atsurae`.

Yes. The README provides a Claude Desktop configuration using `python -m atsurae`, `ATSURAE_API_URL`, and `ATSURAE_API_KEY`.

The documented configuration uses `ATSURAE_API_URL` for the atsurae.ai API endpoint and `ATSURAE_API_KEY` for authentication.

The documented output is an MP4 at 1920x1080 and 30fps, with H.264 video and AAC audio.

Related MCP servers

All in Multimedia Process All alternatives
Add a badge to your READMEShow it’s listed — live SVG, Markdown or HTML.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:
Live Dynamic SVG PreviewAtsurae AllMCPs Directory Badge
Markdown (GitHub README)
[![AllMCPs](https://allmcps.com/api/badge/1000ri-jp-atsurae?style=directory)](https://allmcps.com/mcp/1000ri-jp-atsurae)
HTML Embed
<a href="https://allmcps.com/mcp/1000ri-jp-atsurae"><img src="https://allmcps.com/api/badge/1000ri-jp-atsurae?style=directory" alt="Atsurae on AllMCPs" /></a>