CloudCompareMCP
Demo of CloudCompare MCP (you have to customize the code yourself)
✨ Custom MCP Integration for CloudCompare✨
I've created a homemade Model Context Protocol (MCP) by integrating a Large Language Model (LLM) with CloudCompare. 🚀 Now, I can simply type natural language commands to trigger CloudCompare functions—no more tedious digging through documentation! 📚
🧠 What's MCP? It's a trending approach that converts natural language prompts into executable software commands, bridging human intuition and software automation seamlessly (though not quite at true intelligence yet!).
🛠️ Example commands I typed directly:
- 🔹 "Please subsample the selected point cloud randomly with 1000 points."
- 🔸 "Please set the point size to 10."
- 🟡 "Set the point colors to yellow."
- 🚫 "Please hide the selected point cloud."
- 📦 "Please create a new cube with length 5."
- 📦 "Please create a new cube with length 10."
https://github.com/user-attachments/assets/68ae63e3-de55-43d5-b91b-fa0666e53351
🔍 Current Challenges:
- No existing MCP server for CloudCompare, so I had to start from scratch.
- Fortunately, CloudCompare provides well-sorted documentation and - practical examples.
- The accuracy of generated code still heavily relies on trial and error.
- Prompt engineering requires careful tuning to ensure LLM-generated code matches the desired style.
- Limited context window makes it tough to include the whole stub (.pyi) files or examples.
🚩 Possible Solution: An effective approach might involve providing exhaustive code templates. The LLM can easily fill in parameter values based on these examples.
⏳ I currently don't have time to develop this into a full-fledged project, but this experiment shows inspiring potential for integrating LLM into practical workflows!
🌌 Final Thoughts: Real AI extends far beyond today's tools—it's not just a model or strategy but an expansive interactive system of modular units working harmoniously. 🌐🤖
It would be great if the CloudCompare team could take over
