"Google SecOps Chronicle Overview or Information" matching MCP servers:

Aggregates personal events from multiple sources into a unified timeline and exposes them via MCP for AI assistant retrieval.

A local-first personal archive with a read-only MCP gateway that preserves files and AI conversations, derives evidence-linked memory candidates using local models, and enables bounded context retrieval for questions.

Chronicle is a secure, local-first MCP server designed to transform your historical AI chat logs into a persistent memory vault. It seamlessly parses and indexes raw conversation exports from ChatGPT, Claude, and various IDE transcripts directly on your machine. By utilizing dynamic token-stripping, the engine automatically cleans redundant syntax wrappers to save up to 40%

Indexes and searches agent conversation logs from Antigravity and Cursor workspaces, enabling semantic search, token analysis, and benchmarking over local SQLite storage.

A local MCP server that provides persistent, queryable memory for Crusader Kings II tabletop RP sessions, with tools to log and retrieve characters, timeline events, and tomes backed by plain JSON files.

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 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.

Implements an MCP server that interacts with Google Sheets to store and retrieve prompts and ideas, allowing management of textual content with metadata across spreadsheet tabs.

Comprehensive integration with Google Workspace services (Gmail, Calendar, Sheets, Docs, Drive) plus local storage for job tracking and a personal memory system for storing user preferences, behaviors, and goals.

AutoMem MCP provides persistent memory for AI assistants, enabling them to recall information across conversations and platforms with graph-vector retrieval.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

An MCP server that enables Claude to interact with Google NotebookLM via browser automation, allowing management of notebooks, adding sources, and querying information.

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 persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

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.

MCP server for Google NotebookLM enabling notebook, source, and audio overview management with hybrid metadata storage.

Enables AI agents to query and interact with Google NotebookLM notebooks to retrieve citation-backed information. It provides tools for listing notebooks, accessing source data, and asking natural language questions.

Enables building and managing knowledge workspaces with tools for ingesting content from text, clipboard, or URLs and searching organized knowledge bases. Supports multiple configurable workspaces with separate inbox and knowledge directories for structured information management.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Enables storing durable AI memories linked to local JSON or text sources, automatically superseding outdated structured values, flagging ambiguous changes for review, excluding expired information, and preserving revision history in a local SQLite database.