"Writing about MCP" matching MCP servers:

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.

A Model Context Protocol (MCP) 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.

Enables reading and writing to a Pinecone vector index, including semantic search, document management, and stats retrieval.

A Model Context Protocol server that manages character knowledge and relationships for creative writing projects, offering semantic search and AI-powered analysis.

Universal MCP server for readonly-first access to Oracle, SQL Server, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, and Qdrant vector search.

MCP Memory is a MCP Server that gives MCP Clients the ability to remember information about users across conversations using vector search.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.

An MCP server for providing semantically related Ethereum Improvement Proposals (EIPs) to AI agents.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.

Pinecone Assistant MCP server

An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.