rtlviz 0.2.0

MCP Server for AI-powered RTL diagram generation

RTLViz - AI-Powered RTL Diagram Generator

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"

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/*

License

MIT