Enables AI agents to safely explore directories, read files, search content by pattern or filename, and edit files with checksum verification and dry-run preview within sandboxed filesystem access.
A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.
Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.
MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to:
Submit and validate constraint models
Set model parameters
Solve constraint satisfaction and optimization problems
Retrieve and analyze solution
A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.