"Google Keep" 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.

Reads saved Keep My Prompts library and fetches prompts by name from any MCP client, read-only.

MCP server that lets agents create and update persistent concept maps via the NodeLand API, returning shareable URLs for humans to view, embed, and keep editing.

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.

Technical Preview: local-first decision memory for AI agents. Agents can keep an answer while losing why it was chosen, what was rejected, and what failed; ShadowGraph stores that reasoning and lets callers review decisions against stored facts. Local stdio MCP; install from GitHub with Node.js 20+.

A machine-enforced workflow protocol for AI coding agents that eliminates false progress. Tasks require verifiable evidence to close, while decisions, assumptions, and open questions persist in a versioned reasoning graph for seamless agent handoffs. Designed to keep deep, long-running projects coherent across sessions, agents, and months.

Enables searching Google Scholar with Scholar operators, year ranges, cited-by and all-versions lookups, and retrieving citations in five styles with BibTeX and EndNote export links as structured JSON.

Enables an LLM to keep a persistent scratchpad by adding and reading notes, retrieving the latest note as context, and generating summaries through MCP.

Enables an agent to capture cited sources with extracted bibliographic metadata, keep linked research notes, and search them all. It turns those into BibTeX, annotated Markdown bibliographies, or themed literature-review outlines, stored as plain files inside the project.

Serves project memory to AI agents, providing task-scoped context, tracked changes, validation evidence, and history to keep modifications grounded in current project facts.

Tillpad provides bounded storage and search for agent jobs: bounded KVP storage, RAG search, and wipe receipts so long-running agents keep durable, size-capped context.

Enables AI assistants to keep and retrieve private context locally, with verifiable receipts and optional Solana anchoring for public proofs.

Secure MCP server for querying Google NotebookLM notebooks. Enables AI assistants to list notebooks, read sources, and ask the NotebookLM AI questions about your sources.

Enables coding agents to keep project state, evidence, and decisions bound to the repository, and to resume work across sessions and hosts by receiving a bounded, revalidated context packet instead of a full transcript. It also lets agents record checkpoints, handoffs, and closeouts while keeping deterministic decision and routing authority in code.

A self-hosted MCP server that provides AI assistants with a shared, persistent SQLite-backed memory for storing and retrieving project context, decisions, and discoveries. It enables cross-session continuity and team-wide knowledge sharing to keep AI coding tools aligned and informed.

A local stdio MCP server that stores durable project state — goals, validation evidence, decisions, blockers, checkpoints, and scope coverage — in a single SQLite file alongside the repository. It lets a single agent keep working across context windows, compaction, and restarts by resuming from a saved position instead of a larger context, with deterministic guardrails on goal progression and project completion.

This MCP server provides tools to manage, score, compress, and prune AI agent conversation context, helping keep agents focused and reduce token costs. It is a free, local, pure Python solution for any MCP client.

Local-first personal cognitive engine that provides MCP tools for managing relationships, conversations, agenda, and memory, powered by Ollama.

Enables comprehensive management of Google Ads accounts by allowing users to create campaigns, manage keywords, and generate performance reports through a context-aware interface. It features a three-layer architecture that integrates product knowledge and historical campaign data to support intelligent decision-making.

A frontier knowledge base that’s minimal extensible and agent-native. Built to keep your team’s shared context current and inspectable.