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Manim MCP

Manim MCP

Local
github.com/wstcpytMedia & Design19Updated 77w ago

About Manim MCP

Manim MCP is a Docker-based environment for mathematical animations with Manim, offering both a command line interface and a web API. It lets AI assistants create and run Manim scripts over MCP, suited to users who need a reproducible rendering setup.

Key features

  1. 01Docker-isolated Manim runtime environment
  2. 02Command line rendering mode
  3. 03HTTP web API with interactive docs
  4. 04Script upload and animation file download
  5. 05MCP support for AI assistant access

Use cases

  1. 01Generating Manim scripts from natural language descriptions
  2. 02Rendering animations in a container and retrieving download links
  3. 03Browsing and managing generated media files

MCP support

The project runs as an MCP server that exposes script creation, animation rendering and media file management to AI assistants.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"manim": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"pull"
]
}
}
}
Run commanddocker run -i --rm pull

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
ocipull

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Manim MCP

License: Apache 2.0 Docker Manim API

A Docker-based environment for creating mathematical animations with Manim, featuring both a CLI interface and a web API with Model Context Protocol (MCP) support for AI assistants.

📑 Overview

This project provides:

  1. Containerized Manim Environment: Run Manim in an isolated, reproducible Docker environment
  2. Web API: Create and manage Manim animations via HTTP requests
  3. MCP Integration: Direct interaction with AI assistants like Claude
  4. File Management: Upload scripts and download generated animations

🚀 Quick Start

Prerequisites
  • Docker and Docker Compose installed on your system
Installation
Option 1: Use the Prebuilt Image (Recommended)

Simply pull the prebuilt image from Docker Hub:

docker pull wstcpyt/manim-docker-mcp:latest

Then run it with docker-compose:

docker compose up -d
Option 2: Build Locally
  1. Clone the repository:

    git clone https://github.com/YOUR_USERNAME/manim-docker-mcp.git
    cd manim-docker-mcp
    
  2. Build the Docker images:

    docker compose build
    
Usage
CLI Mode

Create a Python file in the animations directory (see example below), then run:

docker compose run manim -pql animations/example.py ExampleScene
API Mode

Start the API server:

docker compose up -d manim-api

Access the API documentation at http://localhost:8000/docs

🎬 Creating Animations

Basic Example

Create a file animations/example.py:

from manim import *

class CircleToSquare(Scene):
    def construct(self):
        circle = Circle()
        circle.set_fill(BLUE, opacity=0.5)

        square = Square()
        square.set_fill(RED, opacity=0.5)

        self.play(Create(circle))
        self.wait()
        self.play(Transform(circle, square))
        self.wait()
Running the Animation
# CLI mode with preview (-p), low quality (-ql)
docker compose run manim -pql animations/example.py CircleToSquare

# API mode
curl -X POST "http://localhost:8000/run-manim?filepath=/manim/temp/circle_example.py&scene_name=CircleToSquare&quality=low_quality"

📂 Project Structure

manim-docker-mcp/
├── animations/           # Manim animation scripts
├── app/                  # FastAPI application
├── media/                # Generated animations (CLI mode)
├── output/               # Generated animations (API mode)
├── temp/                 # Temporary files
├── uploads/              # Uploaded animation scripts
├── Dockerfile            # Docker image definition
├── docker-compose.yml    # Docker Compose configuration
└── README.md             # This file

🔧 Configuration

Quality Settings
FlagResolutionFrame RateBest For
-ql480p15fpsQuick previews
-qm720p30fpsGeneral use
-qh1080p60fpsPresentations
-qk1440p60fpsProduction videos
Other Useful Flags
  • -p: Preview the output file
  • -t: Transparent background
  • --save_last_frame: Render only the last frame
  • -c COLOR: Set background color

🌐 API Documentation

Core Endpoints
List Files
GET /list-files?directory=/manim
Write File
POST /write-file?filepath=/manim/temp/example.py
Run Animation
POST /run-manim?filepath=/manim/temp/example.py&scene_name=CircleToSquare
Download Animation
GET /download-file?filepath=/media/videos/example/480p15/CircleToSquare.mp4

Full API documentation is available at the /docs endpoint.

🤖 AI Assistant Integration (MCP)

This project supports the Model Context Protocol (MCP), enabling AI assistants to:

  1. Create Manim scripts based on natural language descriptions
  2. Run animations and provide download links
  3. Browse and manage generated media files

Example MCP session:

User: Create an animation showing a circle morphing into a square
AI: I'll create that for you...

🔍 Advanced Usage

Custom LaTeX

The container includes a minimal LaTeX installation. Custom LaTeX can be used in animations:

formula = MathTex(r"\int_{a}^{b} f(x) \, dx = F(b) - F(a)")
self.play(Write(formula))
Mounting Custom Directories

Modify the docker-compose.yml file to mount additional directories:

volumes:
  - ./my_custom_dir:/manim/custom

🛠️ Troubleshooting

Common Issues
  • Docker not running: Make sure Docker daemon is running
  • Permission errors: The container needs write access to mounted volumes
  • Missing media: Check the correct output directory (media/ for CLI, output/ for API)
Getting Help

If you encounter issues:

  1. Check the Manim documentation
  2. Search existing GitHub issues
  3. Create a new issue with details about your problem

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgements

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.