"Managing Docker Containers and Applications" matching MCP servers:

Provides Prolog logic programming integration for Claude via a Docker SWISH container, with persistent notebooks and cognitive architecture support.

Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.

Enables Claude and ChatGPT to share a single study-progress database for interview prep, coursework, and spaced repetition, with per-user data isolation and server-side SM-2 scheduling for due reviews and study plans.

TheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.

Enables local MCP clients to list, read, write, and search notes in an Obsidian vault via an HTTP server running inside Obsidian.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Engram is a local-first memory server that lets AI agents remember things across sessions with zero cloud dependencies.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

Enables coding agents to connect to a user's DevVault, allowing them to search, retrieve, save, and append research/context containers directly from the agent.

Graph-based MCP server for persistent AI memory, session checkpointing, context compression, and cross-session context management for LLM applications.

Enables AI applications to add, search, update, and delete long-term memories using the Mem0 Memory API, allowing agents to persistently remember user preferences, conversation history, and contextual information across sessions.

Enables LLM agents to access a private knowledge base through MCP by automatically chunking and indexing .txt documents, with zero configuration and no Docker or vector database required.

Local-first MCP graph intelligence server providing RRF hybrid search, multi-hop traversal, source snippets, and rationale nodes for AI agents, without Docker or web UI.

An MCP server implementation that enables interaction with the Letta API for managing agents, memory blocks, and tools in the Letta system.

MCP server for Yandex Wiki with full-text search. Read and write pages, comments, attachments, and dynamic tables (grids); optional server-side read-only mode for agents. Docker-ready.

A neurosymbolic AI server combining Prolog’s symbolic reasoning with Model Context Protocol (MCP) for hybrid AI applications.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.