"Buy Me A Coffee" matching MCP servers:

MCP server exposing memory search, index, and stats tools for agents, with honesty guards to prevent re-litigation of settled decisions.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

A long-horizon memory architecture for AI agents, providing a scalable, graph-based memory with causal typing and an MCP interface.

A long-term memory MCP server for AI agents that stores memories (facts, decisions, etc.) in a single SQLite database with hybrid search and full edit history, ensuring consistency across sessions.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

Enables semantic search over local markdown note collections using vector embeddings, with real-time file watching and zero-config setup.

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.

Provides LLM clients with a persistent, scalable knowledge graph memory system that supports semantic retrieval, contextual recall, and temporal awareness.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

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.

A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.

A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

RAGX MCP Server enables retrieval-augmented generation with document ingestion, hybrid search, and agentic answering using Claude, exposing tools for querying, searching, and managing documents.

A local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.