rtlviz 0.2.7

MCP Server for AI-powered RTL diagram generation

RTLViz - AI-Powered RTL Diagram Generator

⚠️ RtlViz can make mistakes, so double-check it.

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 for your IDE, configure manually:

Claude Desktop — claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json

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

Claude Code (VS Code Extension) — ~/.claude/settings.json

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

VS Code Copilot — .vscode/mcp.json

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

Cursor — ~/.cursor/mcp.json

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

Windsurf — ~/.codeium/windsurf/mcp_config.json

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

Quick Start Guide

Step 1: Install the Package

pip install rtlviz

Step 2: Run Setup

rtlviz-setup

This automatically configures RTLViz for all your AI IDEs (Claude Desktop, Claude Code, VS Code Copilot, Cursor, Windsurf).

Step 3: Ask Your AI to Generate a Diagram

Open your AI IDE and ask:

"use rtlviz to generate block diagram for [your-project-path]"

Step 4: Get Your Interactive Block Diagram

The AI analyzes your RTL code and generates a publication-quality block diagram:

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

How It Works

RTLViz uses the Model Context Protocol (MCP) to seamlessly integrate with your AI IDE:

1. Local MCP Server — A lightweight server runs locally, exposing RTL analysis capabilities to your AI assistant
2. Skill Injection — The AI dynamically loads the rtlviz://prompt resource, which contains expert RTL analysis heuristics
3. Semantic RTL Parsing — The AI performs deep analysis of your Verilog/SystemVerilog, tracing signal dependencies and identifying pipeline stages
4. Graphviz DOT Generation — Architecture is encoded as DOT with semantic clustering, orthogonal routing, and component-specific styling
5. Interactive Rendering — The render_diagram tool generates a zoomable, pannable HTML5 viewer with Viz.js

Zero infrastructure. No API keys. 100% local execution.

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