Why this server?
Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context
Why this server?
Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Why this server?
Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
Why this server?
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Uses Ollama or OpenAI to generate embeddings.
Why this server?
A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
Why this server?
A Model Context Protocol server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.
Why this server?
This TypeScript-based server implements a simple notes system, allowing users to create and manage text notes and generate summaries, showcasing core MCP concepts.
Why this server?
A server implementation that allows AI assistants to read, create, and manipulate notes in Obsidian vaults through the Model Context Protocol.
Why this server?
Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and ownership of your data. Integrates directly with Obsidan.md