"fetch" matching MCP servers:

Enables indexing a folder of documents or uploaded files into a Qdrant vector store and retrieving relevant chunks with cosine similarity, metadata filters, and heuristic reranking. Exposes rag_stats, rag_search, and rag_index tools over HTTP and Streamable HTTP MCP so clients can fetch ready-to-use context blocks without any built-in chat model.

Exposes a shared long-term memory store over the Model Context Protocol so that Trae, WorkBuddy, and any other MCP client can save, search, list, fetch, and forget memories in the same on-disk JSONL store used by the DSH plugin, with no need to run DSH. Supports keyword plus local-vector semantic retrieval, project/global scoping, and cross-process locking so records written by any client are immediately visible to the others.

Exposes a markdown knowledge base to MCP clients so they can search pages, fetch individual documents, and ask grounded questions that come back with numbered citations or a refusal. Retrieval is graded before answering, with a single query rewrite before giving up when nothing relevant is found.

Enables semantic search and document retrieval from OpenAI Vector Store, allowing users to search documents using natural language queries and fetch complete document contents through ChatGPT.

Tracks learning progress and generates personalized daily learning insights using RAG to fetch relevant content from blogs, RSS feeds, and Reddit based on your current topics and learning goals.