rtlviz 0.2.5

MCP Server for AI-powered RTL diagram generation

RTLViz - AI-Powered RTL Diagram Generator

⚠️ Development Notice: This project is under active development. It can make mistakes. Please verify generated diagrams against your design.

An MCP (Model Context Protocol) server that enables AI assistants to generate publication-quality RTL block diagrams from Verilog/SystemVerilog code.

Installation

pip install rtlviz
rtlviz-setup

That's it! The setup command auto-configures your AI IDE (Claude Desktop or VS Code).

Manual Setup (Alternative)

If auto-setup doesn't work, you can configure manually:

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "rtlviz": {
      "command": "rtlviz-server"
    }
  }
}

VS Code (.vscode/mcp.json):

{
  "servers": {
    "rtlviz": {
      "command": "rtlviz-server"
    }
  }
}

Usage

Just ask your AI: "Generate an RTL diagram for CPU.v"

💡 Recommended: Works best with latest AI models like Claude Opus 4.5 or Claude Sonnet 4.5 for accurate RTL analysis.

How It Works

1. You install the package (pip install rtlviz)
2. Your IDE spawns the server locally when you open a session
3. The AI reads the rtlviz://prompt resource to learn how to analyze RTL
4. The AI generates Graphviz DOT code based on your Verilog
5. The AI calls the render_diagram tool to create an interactive HTML viewer

No server hosting required. No API keys. Runs 100% locally.

Enterprise & Privacy

• Safe for Work: All RTL analysis happens locally or via your enterprise-approved LLM provider.
• Telemetry: We collect minimal, anonymous usage data (version, session ID) to improve the tool.
  • No IP addresses or personal data.
  • No file contents or code.
• Opt-Out: Set the environment variable RTLVIZ_TELEMETRY=0 to disable all network calls.
  • Firewall Friendly: If blocked, the tool fails silently and continues working.

Developing & Releasing

Analytics Setup

To enable your own analytics dashboard:

1. Deploy analytics/google_apps_script.js as a Google Web App (Execute as Me, Access: Anyone).
2. Set RTLVIZ_TELEMETRY_URL in src/rtlviz/telemetry.py to your Web App URL.

Publishing to PyPI

1. Bump Version: Update pyproject.toml and src/rtlviz/telemetry.py.
2. Build: python -m build
3. Upload: twine upload dist/*

Developers

• Naveen Venkat - [e-mail]
• Hariharan - [e-mail]

License

MIT