Summary

Dev3000 addresses the complexity of debugging modern web applications by capturing a comprehensive, unified, and timestamped timeline of the entire development process. It targets developers seeking more efficient debugging workflows, enabling AI assistants to analyze server logs, browser events, network requests, and automatic screenshots chronologically, significantly speeding up issue resolution.

How It Works

The tool monitors a web application within a real browser environment, systematically capturing server logs, browser console messages and errors, network requests and responses, and generating automatic screenshots during navigation, errors, or key events. This data is aggregated into a single, timestamped feed. An optional MCP (Multi-Channel Protocol) server provides an API for advanced querying of logs and direct control over the browser, allowing AI agents to execute actions like clicking, typing, or evaluating code.

Quick Start & Requirements

• Install: pnpm install -g dev3000
• Run: dev3000
• Prerequisites: Requires a Node.js environment with pnpm. The MCP server defaults to port 3684, and the application port defaults to 3000.
• AI Integration: Connects to AI models via the MCP server, e.g., claude mcp add dev3000 http://localhost:3684/api/mcp/mcp.

Highlighted Details

• Captures server logs, browser console, network requests, and screenshots in a unified feed.
• Facilitates AI-driven debugging by providing a complete, chronological development context.
• MCP server enables programmatic browser control and advanced log querying.
• Visual timeline available at http://localhost:3684/logs.

Maintenance & Community

No specific details regarding maintainers, community channels, or project roadmap were provided in the source material.

Licensing & Compatibility

The license type and compatibility for commercial use or closed-source linking are not specified in the provided description.

Limitations & Caveats

The effectiveness of the AI debugging feature is dependent on the capabilities and integration of the chosen AI model. The tool's primary function relies on the comprehensive capture of events, and any gaps in monitoring could impact debugging accuracy.