Automate file system operations through natural language commands powered by an LLM.
This project provides a minimal example of automating file system tasks by integrating a Large Language Model (LLM) like Ollama with a Multi-Agent Communication Protocol (MCP) tool via a Python agent bridge. It establishes a pipeline where user's natural language commands are processed by the LLM, translated into structured tool calls, executed by an MCP server, and finally performed by a backend REST API that handles actual file system operations. This architecture allows for natural language control over file and directory management.