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, which is ideal for a local knowledge base.
Why this server?
Provides structured memory management across chat sessions, allowing Claude to maintain context and build a knowledge base within project directories. This would enable long-term storage and retrieval of information within your local knowledge base.
Why this server?
Enables integration with Google Drive for listing, reading, and searching over files, supporting various file types with automatic export for Google Workspace files, useful for accessing documents in your cloud storage and incorporating them into the local knowledge base.
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, suitable for building a semantic search on top of a knowledge base.
Why this server?
Allows LLMs to programmatically interact with Logseq knowledge graphs, allowing creation and management of pages and blocks. Logseq is a local-first knowledge base app, so this MCP would allow programmatic management of a local knowledge base.
Why this server?
Enables LLMs to interact with Notion workspaces, providing capabilities like searching, retrieving, creating and updating pages, as well as managing databases. This could be used if your knowledge base exists in Notion.