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Why this server?
Enables integration with Google Drive for listing, reading, and searching over files, supporting various file types with automatic export for Google Workspace files like Google Docs and Sheets.
Why this server?
A Model Context Protocol (MCP) connector that allows AI agents to directly interact with Google Sheets, enabling creation, reading, and modification of spreadsheets through natural language.
Why this server?
Allows interaction with Google Calendar through Claude Desktop using the Model Context Protocol, enabling calendar event management and schedule analysis. This is related to Google Workspace.
Why this server?
A Model Completion Prompt (MCP) plugin that allows users to create events in Google Calendar with customizable attributes like times, description, location, attendees, and timezone. Related to Google Workspace.
Why this server?
A Model Context Protocol server that enables AI assistants to interact with Microsoft 365 services (Excel, Calendar, Mail, OneDrive, Teams, etc.) through the Graph API, allowing for management of collections and documents.
Why this server?
Allows AI assistants to generate and transform high-quality images from text prompts using Google's Gemini model via the MCP protocol.
Why this server?
A Model Context Protocol server that enables AI assistants to perform GitHub operations including repository management, file operations, issue tracking, and pull request creation. This could be helpful to organize and work on Google related projects or documentation.
Why this server?
MCP (Model Context Protocol) server that utilizes the Google Gemini Vision API to interact with YouTube videos. It allows users to get descriptions, summaries, answers to questions, and extract key moments from YouTube videos.
Why this server?
A Model Context Protocol server that analyzes YouTube videos, enabling users to extract transcripts, generate summaries, and query video content using Gemini AI.
Why this server?
A Model Context Protocol server that connects to Google Cloud services, allowing users to query logs, interact with Spanner databases, and analyze Cloud Monitoring metrics through natural language interaction.