"Using Google Sheets, Google Drive, and Google Docs" 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.

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 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.

Gives your agent a private Kinematic Drive: a deterministic memory engine for sub-millisecond exact recall over code and docs with no hallucination. Also provides self-serve checkout and metered pay-per-call tools for AI agents.

A structured document management system using MCP protocol to handle large requirement documents by modularizing content, enabling token-efficient queries and consistency through hierarchical nodes and metadata.

An MCP server for team documentation and knowledge bases, enabling semantic search over documentation files using local embeddings.

MCP server for a shared Postgres-backed knowledge base with hybrid retrieval and agentic RAG, enabling coding agents to upload, search, and ask questions over documents with cited answers.

MCP server that syncs Markdown docs from a GitHub repository and provides search, reading, and listing capabilities for AI agents.

Enables MCP-capable agents to query a LightRAG knowledge graph for grounded answers with source files, ingest documents into the index, and retrieve indexing statistics.

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.

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.

MCP server that gives Claude Code and other MCP clients persistent memory using plain Markdown notes stored on your disk and optionally synced to cloud storage (iCloud, OneDrive, Google Drive, Dropbox).

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.

A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.

MCP server that integrates Google Drive, RAG, and memory services for context-aware knowledge management, enabling session, project, document, catalog, and knowledge operations for AI assistants.

Enables AI tools to query context from a local JSON data source via stdio, demonstrating the Model Context Protocol.

Enables researchers to turn property-history questions into reviewable evidence dossiers by gathering sources, tracing people and places, linking conclusions to supporting or opposing passages, and exporting portable backups.

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.