"Managing Google Calendar" matching MCP servers:

A code-first MCP server that provides a single tool to execute JavaScript or TypeScript with authenticated access to Google Workspace APIs. It enables flexible interactions with services like Calendar and Drive by running user-provided scripts through a built-in runtime environment.

Enables MCP-compatible AI assistants to run Python code on Google Colab GPU/TPU runtimes, supporting accelerators like T4, A100, H100, with background execution and Google Drive integration.

Enables AI assistants to create and modify Google Apps Script projects, manage versions and deployments, run functions, and inspect execution history through natural language.

An advanced MCP server that provides an agentic interface to Google's Gemini 3.1 models via Vertex AI, combining real-time Google Search, URL analysis, and Python code execution to solve complex multi-step research and data tasks.

A Model Context Protocol server that enables interaction with PostgreSQL databases for analyzing setups, debugging issues, managing schemas, migrating data, and monitoring performance.

Enables automation of Google Jules AI coding assistant through task creation, code review automation, repository management, and AI-powered development workflows. Supports multiple session modes including cloud deployment with persistent authentication.

A Model Context Protocol server implementation that enables secure remote command execution via SSH, with features for managing and using SSH credentials.

Enables users to create, manage, and execute Google Apps Script projects through natural language. It provides comprehensive tools for code editing, function execution, deployment management, and monitoring script processes.

A comprehensive learning project demonstrating MCP server implementation with tools for calculator operations, file management, and system information retrieval, including Google ADK integration patterns and best practices.

Lets any MCP-compatible agent use Google Colab or Jupyter runtimes as an execution backend, enabling code execution, notebook management, package installation, hardware introspection, training jobs, and file handling.

Provides integration with the Google Apps Script API, allowing management of script projects, deployments, versions, and executions through any MCP-compatible client like Claude Desktop or VS Code.

A secure MCP (Model Context Protocol) server hosted on Google Cloud Run that enables team collaboration by providing authenticated access via Google Cloud IAM, allowing teams to share custom MCP servers over the internet before official MCP authentication is implemented.

Provides a persistent Python REPL session as a tool for executing code, managing files, installing packages, and initializing projects via the MCP protocol.

A self-hosted MCP server providing Claude with an interactive Jupyter environment, including real kernels, persistent notebooks, and optional GPU offload to Google Colab.

Enables WebGPT to delegate durable coding-project tasks to a local Luna worker over MCP, managing sessions, tools, permissions, memory, and recovery while preserving at-most-once execution.

A starter template for creating MCP servers that work with Puch AI, featuring ready-to-use tools for job searching and image processing. Includes examples for Bearer token authentication, OAuth integration with Google and GitHub, and demonstrates user-scoped data management.

A FastMCP server exposing 22 tools for calendar, to-do, notes, web search, math, scratchpad, task queue, and sandboxed code execution, designed for safe RL training with structured outputs and FastMCP transforms.

An MCP server that allows managing Docker containers through natural language, enabling users to compose, introspect, and debug containers without running commands themselves.

A Model Context Protocol server that allows integration with Claude Desktop by creating and managing custom tools that can be executed through the MCP framework.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution