"New York Times" matching MCP servers:

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Enables Claude to search a local hybrid retrieval index of research papers and ingest new PDFs, providing research-paper memory queryable directly through natural language.

MCP server that enables semantic search over local PDF collections using local RAG, with automatic indexing of new documents.

Provides persistent, graph-based memory for AI agents using Neo4j and vector embeddings, enabling semantic recall across sessions via MCP.

Provides a self-hosted knowledge index with document-level permissions, enabling AI agents to retrieve exactly the documents they are authorized to see via MCP. Supports OAuth 2.1, custom embedding models, and runs inside your network.

Enables AI agents to search and retrieve relevant document content from existing embeddings stored in Supabase vector database. Provides semantic search capabilities to find document chunks based on similarity to query text without generating new embeddings.

Enables Claude to recall and query past conversations through a local knowledge graph and vector index, with tools for searching memory, retrieving related entities, and adding new memories.

A vector-DB MCP server that gives Claude conversational memory by storing solved problems and solutions as vectors and retrieving relevant ones when a new request resembles a past solution.

A Model Context Protocol server that searches transcript segments in a Turso database using vector similarity, allowing users to find relevant content by asking questions without generating new embeddings.