Why this server?
SourceSage is an MCP server that efficiently memorizes key aspects of a codebase—logic, style, and standards—while allowing dynamic updates and fast retrieval. It's designed to be language-agnostic.
Why this server?
Memory Bank Server provides a set of tools and resources for AI assistants to interact with Memory Banks, which are structured repositories of information that help maintain context and track progress across multiple sessions.
Why this server?
A Model Context Protocol (MCP) server that provides hierarchical task management capabilities to Large Language Models. This tool provides LLMs with the structure and context needed to manage complex tasks and dependencies.
Why this server?
A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
Why this server?
Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.
Why this server?
A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
Why this server?
A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
Why this server?
This project implements a Model Context Protocol (MCP) server for connecting AI models with Obsidian knowledge bases. Through this server, AI models can directly access and manipulate Obsidian notes, including reading, creating, updating, and deleting notes, as well as managing folder structures.
Why this server?
Provides tools for listing and retrieving content from different knowledge bases using semantic search capabilities.