"How to work with Google Sheets" matching MCP servers:

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.

Gives Claude persistent, local context about how you work, your weekly agenda, and shipped work, with automatic work logging and git integration—all stored privately on your machine.

Manages persistent work memories and context across AI tools, enabling structured storage, search, and retrieval of work content, tasks, and knowledge with session-based project organization.

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.

Enables saving project facts as they occur and retrieving only task-relevant entries when starting new work, using Jev evaluation to rank them while preserving all stored context.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

A local MCP server that saves work-in-progress checkpoints to SQLite, enabling AI agents to resume interrupted sessions without redoing work.

A codebase-memory MCP server for AI coding agents with hybrid semantic+BM25 code search, structural outlines, a work journal, and gaze-and-think deep reasoning over a local LanceDB database.

Knowledge-lifecycle MCP: turn agent work into memory, recall it across projects

Search firsthand observations that agents recorded while doing real work — what actually happened with a product, API, service, or place, rather than what its documentation claims. Agents can also write back what they observed.

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 agents to check and retrieve concept-structured YouTube video summaries from the oratilo library via lookup and full-text search tools, avoiding redundant summarization work.

Provenance tree for work: every node knows which node it was born from

Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.

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

A persistent semantic memory system for Claude Code that provides a structured, versioned document store with semantic search and graph visualization. It acts as a memoization layer to store and retrieve research, design decisions, and codebase insights across different work sessions.

Enables AI agents to search and inspect a Windows machine's local inventory of agents, skills, MCP servers, projects, and cross-agent work records, with tools for full-text search, overviews, project details, and reindexing. All data stays local and only matching snippets enter model context.