Runware MCP Server

AI Media Generation for Any MCP Client

What Is This?

An MCP server that connects your AI coding assistant directly to Runware's AI media generation infrastructure. Works with any client that supports the Model Context Protocol.

View diagrams on GitHub

One config. Zero complexity. Infinite creativity.

Installation

Add the server to your MCP client config:

{
  "mcpServers": {
    "runware": {
      "command": "npx",
      "args": ["-y", "runware-mcp"],
      "env": {
        "RUNWARE_API_KEY": "your-api-key"
      }
    }
  }
}

Config locations by client

Note: Config format may vary slightly by client. Check your client's MCP documentation for exact syntax. Codex CLI uses TOML — see the Codex MCP docs for setup.

That's it. No pip install. No virtual environments. No Python version conflicts. Just paste, restart, create.

Capabilities at a Glance

View diagrams on GitHub

The Complete Toolset

Image Generation & Manipulation

Video Generation

Audio Generation

Creative Tools

Utilities

Batch Operations

Provider-Specific Features

This server exposes the full capabilities of each provider:

MCP Resources

Access your generated content programmatically:

Prompt Templates

Pre-built workflows for common tasks:

Documentation

This MCP server includes comprehensive built-in documentation accessible as MCP resources. Any connected client can discover and read detailed API references, feature guides, and provider docs on demand.

42 documentation resources organized across 5 categories:

Example URIs: runware://docs/tools/image-inference, runware://docs/features/controlnet-guide, runware://docs/providers/kling-ai, runware://docs/guides/cost-optimization

Ask your AI assistant to list or read any runware://docs/* resource for detailed reference information.

Architecture

View diagrams on GitHub

What's Included

A ground-up TypeScript rewrite covering 100% of the Runware API.

Video Model Comparison

ControlNet Preprocessors

Configuration Reference

Security

Example Workflows

Product Photography Pipeline

You: "Process all images in /photos/products - remove backgrounds,
      upscale 2x, and save as PNGs to /photos/processed"
Assistant uses processFolder with removeBackground and upscale pipeline

Video Storyboard

You: "Create a 3-part video story: sunrise over mountains,
      eagle soaring, landing on a branch. Use Kling, 5s each."

Assistant generates 3 coordinated videos with consistent style

Brand Asset Generation

You: "Generate 5 logo variations for 'TechFlow' - modern, minimal,
      tech-focused. Then vectorize the best one."

Assistant uses imageInference, selects best, then vectorize to SVG

Music + Video

You: "Create a 30-second promo video with matching background music"

Assistant uses videoInference + audioInference in parallel

Development

git clone https://github.com/runware/mcp-server
cd mcp-server
npm install
npm run build
npm test

Contributing

See CONTRIBUTING.md for the full development guide including project structure, how to add new tools, testing patterns, and code style.

1. Fork it
2. Create a feature branch
3. Make it pass: npm run build && npm run lint && npm test
4. Submit PR

For version history and release notes, see CHANGELOG.md.

License

MIT