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AI-Powered Documentation Generator for Legacy Codebases

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AI-Powered Documentation Generator for Legacy Codebases

As a former CIO who managed teams working with millions of lines of legacy code (Visual Basic, Sybase, Oracle Forms, and worse), I feel the pain of maintaining and onboarding developers to legacy systems. Believing that LLM-enabled tools can play a role in solving this, I've built a tool that automatically generates documentation for legacy codebases using the Model Context Protocol (MCP) & Claude Sonnet. At first glance, I think this approach has merit. Some samples are in the README. I welcome your thoughts. The Problem: - Legacy codebases are notoriously difficult to understand and navigate - Onboarding new developers takes months - Making changes safely requires deep knowledge of the system - Business stakeholders lack visibility into system architecture The Solution - an MCP-based tool that: - Scans your codebase - Generates README files at each level of the directory structure - Creates C4 architecture diagrams showing system components and relationships - Builds a complete documentation hierarchy from high-level architecture to implementation details The tool aims to helps teams: - Onboard developers faster with clear system documentation - Make changes confidently with better understanding of components - Communicate system architecture to stakeholders - Maintain living documentation that evolves with the codebase Have a look / try it out! GitHub: https://github.com/jonverrier/McpDoc License: MIT To credit various other similar works: https://news.ycombinator.com/item?id=43154065 ( jtwaleson's post) https://news.ycombinator.com/item?id=42521769 https://news.ycombinator.com/item?id=41393458

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, mcp · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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