"Get the current date and time" matching MCP servers:

The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.

Lets AI assistants understand what you're working on — current screen content, recent dictation, clipboard, and saved notes — running entirely on your own machine with nothing sent to the cloud.

A read-only MCP server that lets you search current and historical facts from a time-indexed knowledge graph derived from a folder of Markdown/text documents, with verification against original sources. It exposes tools for status, fact search, history search, and revision lookup while keeping document insertion and modification to the CLI.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.

Gives every AI agent working in a repository a shared, persistent knowledge graph stored in a plain JSONL file, with each entity, relation, and observation tagged with the date and agent that added it. Provides tools and prompts for searching, editing, renaming, merging, cleaning up, and exporting that memory, and keeps it locked to the repository that owns it.

Your AI forgets everything between sessions. Rekindle fixes that. For Claude Code users who lose time re-explaining project context every session.

A local MCP server that gives saved bookmarks a lifecycle with time-based layers (pending, watching, kept, lapsed), so they get decided on instead of accumulating. It aggregates bookmarks from your browsers, drips them into a daily queue, and supports keep/watch/drop decisions, restore, and undo.

Persistent, structured memory server for AI agents using the Model Context Protocol, with categorized storage and tools to add, get, delete, and list memories.

MCP server for tea enthusiasts to get daily tea recommendations, manage tea inventory, and fine-tune brewing parameters based on taste feedback.

Provides shared real-time context for Claude Code agents, including scope awareness, prior decisions, and anti-overlap, using only files in the repo.

Enables coding agents to store, search, update, and delete durable facts, preferences, decisions, and plans in a shared local SQLite database, along with per-namespace profile get/set and stats. Uses offline lexical FTS5 search so multiple agents working on the same project can share one memory store.

Enables agents to retrieve their current identity from IDENTITY.md and route durable lessons, preferences, rules, and corrections to the appropriate standing file over MCP stdio.

Persistent AI agents memory for Claude, Codex, Cursor... that is always current.

Provides access to the HUMMBL Base120 framework of 120 validated mental models organized across 6 transformations, enabling users to search, retrieve, and get AI-recommended mental models for problem-solving and decision-making.

Provides a memory server for coding agents over MCP, capturing and retrieving the meaning and rationale behind code decisions down to the function level. It enables agents to search product memory, get task context, and propose new memories to avoid re-deriving or re-breaking past decisions.

MCP server for Airi that manages dynamic mental state via stdio, offering tools to get context, log events, check status, settle, and retrieve mood/energy.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

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.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.