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Why this server?
This server is a memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources, thus allowing connection to a vector knowledge base.
Why this server?
Enables fetching relevant content and embeddings from Supavec via the Model Context Protocol, allowing AI assistants like Claude to access vector search capabilities.
Why this server?
Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
Why this server?
A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
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 fork of the Neo4j Model Context Protocol server that enables interaction with Neo4j knowledge graphs through environment variable configuration and improved options for remote connections.
Why this server?
An MCP server that enables graph database interactions with Neo4j, allowing users to access and manipulate graph data through natural language commands.
Why this server?
An MCP server implementation that enables AI models to discover, search, and analyze data stored in Typesense collections through tools for querying documents, retrieving specific items, and accessing collection statistics.
Why this server?
A Model Context Protocol (MCP) server implementation for DuckDB, providing database interaction capabilities through MCP tools. It would be interesting to have LLM analyze it. DuckDB is suitable for local analysis.
Why this server?
Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.