agenticcontrolio

agenticcontrolio / twincat-validator-mcp

Public

An MCP server that validates, auto-fixes, and scaffolds TwinCAT 3 XML files. Connect it to any LLM client to give your AI assistant reliable, deterministic TwinCAT code quality tooling — structural checks, 21 IEC 61131-3 OOP checks, auto-fix pipelines, and canonical skeleton generation.

14
2
100% credibility
Found Mar 03, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A helper server that lets AI tools like Claude validate, auto-fix, and generate reliable code files for Beckhoff TwinCAT PLC programming.

How It Works

1
📦 Get the code checker

Download a simple helper that makes your AI assistant smart about PLC code files.

2
🔗 Connect to your AI

Add it to your favorite AI coding tool like Claude with one click so it understands PLC files.

3
🛠️ Ask AI to check code

Give your AI a PLC file and say 'check this for errors and fix what you can' – it scans deeply for issues.

4
⚡ Watch auto-fixes happen

Your AI finds problems like bad formats or missing parts, fixes safe ones automatically, and shows a safety score.

5
Review the results
✅
All good!

File scores high and is safe to import into your PLC software.

🔧
Needs a tweak

AI lists exact issues with tips so you can fix quickly.

🚀 PLC code ready

Your clean, validated PLC code loads perfectly, saving hours of debugging.

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Star Growth

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AI-Generated Review

What is twincat-validator-mcp?

This Python MCP server validates, auto-fixes, and scaffolds TwinCAT 3 XML files like .TcPOU and .TcGVL, ensuring they import cleanly into Beckhoff PLCs. It runs 34 structural checks plus 21 IEC 61131-3 OOP rules, delivers 0-100 health scores, and applies deterministic fixes for GUIDs, indentation, and CDATA issues. Hook it to any MCP client—like Claude Desktop, Cursor, or VS Code with GitHub Copilot—for AI assistants that output reliable, compile-ready PLC code.

Why is it gaining traction?

Unlike generic XML linters, it offers idempotent auto-fixes, batch orchestration, and canonical skeletons that survive roundtrips through LLMs, cutting debug time on AI-generated PLC artifacts. MCP integration means zero setup for mcp github copilot vscode users or mcp server ai workflows in n8n; just pip install and add to your mcp github config. Tools like process_twincat_batch enforce validate-fix-validate pipelines with progress tracking.

Who should use this?

Beckhoff TwinCAT developers generating OOP function blocks via AI assistants in Cursor or VS Code. Automation teams enforcing 61131-3 standards across batches of .TcPOU files. PLC project managers tired of manual GUID hunts and import failures in mcp github issues.

Verdict

Grab it if you're building AI-driven TwinCAT workflows—docs and mcp server examples shine, with one-click .dxt for Claude. At 14 stars and 1.0% credibility, it's early but production-stable per classifiers; test on a branch first.

(187 words)

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