2d Games Assets Generator

What is 2d Games Assets Generator?

An MCP server that generates advanced mock 2D PNG assets for game prototypes directly from any MCP-compatible AI client like Claude Desktop. It is engine-agnostic, supporting Godot, Unity, Unreal Engine, GameMaker, and more. Designed for developers to create placeholder sprites, UI elements, health bars, and spritesheets without opening an image editor.

How to use 2d Games Assets Generator?

Install via npx (no install: add {"command": "npx", "args": ["-y", "2d-assets-mcp"]} to your MCP client config) or install globally with npm/pnpm. Three tools are provided: generate_mock_asset, generate_mock_asset_batch, and read_image_metadata. Invoke by describing assets in natural language (e.g., “Create a 128×128 blue rounded-rectangle placeholder…”).

Key features of 2d Games Assets Generator

• Single asset generation with full visual configuration in one tool call.
• Batch and spritesheet mode for multiple assets or animation strips.
• Rich visual options: gradients, patterns, transparency, rounded corners, and borders.
• Progress/health bar fills using fillPercent and trackColor.
• Auto-scaling text that fits the asset; optional explicit fontSize.
• Embedded JSON metadata in PNG EXIF for non-vision AI workflows.
• Automatic filename dimensions (e.g., player_idle_128x128.png).

Use cases of 2d Games Assets Generator

• Rapidly generate placeholder sprites for game characters during prototyping.
• Create UI elements such as buttons and health bars with gradients and labels.
• Produce animation spritesheets (e.g., run cycles) with multiple frames in one request.
• Generate batch assets with consistent styling for levels or power-ups.
• Read embedded asset metadata without loading image pixels for AI understanding.

FAQ from 2d Games Assets Generator

How does this differ from using an image editor or AI image generation?

It provides deterministic, configurable PNG generation via simple text prompts – ideal for rapid prototyping placeholder assets without manual design or generative AI overhead.

What are the runtime requirements?

Node.js and an MCP-compatible client (e.g., Claude Desktop). It can run via npx without installation or