"A simple MCP server or implementation" matching MCP servers:

A lightweight MCP server that provides persistent knowledge graph storage for AI assistants, enabling memory across sessions through entity-relationship storage with JSON file persistence.

Provides AI coding agents with persistent memory by recording sessions and normalizing them into a searchable knowledge graph, then delivering relevant context at the start of the next session.

An MCP server that tracks AI prompts in Claude Code and automatically annotates git commits with the history of what was asked. It provides tools to query session summaries, retrieve uncommitted work, and manage AI provenance directly within Claude.

Provides AI assistants with persistent memory across sessions using local SQLite and keyword search, allowing storage and retrieval of user preferences, project context, and decisions.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI assistants to store and retrieve persistent memories with a web management interface. Supports creating, searching, and managing memories through natural language commands or a visual web dashboard.

Personal memory MCP server backed by SQLite FTS5 BM25 search with temporal reranking.

Self-hosted knowledge manager and MCP server that lets you organize snippets, questions, and documents via a web UI and exposes them to AI assistants for reading and writing.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables AI tools to query context from a local JSON data source via stdio, demonstrating the Model Context Protocol.

Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.

Enables AI assistants to persistently add, read, and summarize sticky notes through MCP-compliant tools, resources, and prompts.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

A local MCP server that allows AI systems to search and retrieve information from a custom knowledge base generated from markdown files. It provides tools for natural language text search, category browsing, and specific content chunk retrieval.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.