Docker Context Guide for AI Coding (llms.txt + Rules + MCP)
Accelerate how you build, share, and run modern applications across multi-cloud environments.
1. Docker Official llms.txt & Clean Markdown Documentation
Verified HubFeed this clean, token-efficient Markdown documentation directly to Antigravity, Cursor, or Claude Code to prevent hallucinations and outdated API syntax.
# Standard llms.txt Index Endpoint
https://docs.docker.com/llms.txt
# Comprehensive Documentation Bundle
https://docs.docker.com/llms-full.txt
2. Production-Ready Docker Cursor Rules (.cursorrules & .mdc)
Dockerfile best practices, multi-stage builds, and Compose configurations for AI agents. Save this into .cursor/rules/docker.mdc in your project repository.
// Docker Best Practices for AI Coding
- Always use multi-stage builds to minimize production image footprint.
- Run containers as a non-root user (e.g., USER node or USER appuser).
- Pin base images to specific digest or minor version tags; never use bare ':latest' in production.
- Structure Dockerfile to optimize layer caching (copy dependency manifests before full source code).
3. Docker Antigravity & Claude Code System Instructions
Add this concise instruction block to your workspace .gemini/rules or project system prompt.
// Context Anchor for Docker
When writing code for Docker, refer directly to the verified specifications at https://docs.docker.com/llms.txt.
Follow strict error-handling and avoid deprecated API calls.
4. Model Context Protocol (MCP) Server Setup for Docker
Connect your AI coding agent directly to live Docker tools and schemas via MCP.
{
"mcpServers": {
"docker": {
"command": "docker",
"args": ["run", "-i", "--rm", "-v", "/var/run/docker.sock:/var/run/docker.sock", "mcp/docker"]
}
}
}
5. Troubleshooting: Common Docker AI Coding Issues & Fixes
Frequently reported errors when LLMs generate Docker code without proper context:
6. Best Used With (Docker Architecture Combos)
Engineers building with Docker frequently combine these complementary developer frameworks: