"Google Vertex AI Platform" matching MCP servers:

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

A CLI tool that runs a Model Context Protocol server over stdio, enabling interaction with specification documents like business requirements, product requirements, and user stories for the Specif-ai platform.

MCP server that routes medical questions and images to MedGemma models hosted on Vertex AI, enabling interaction with text-only and multimodal medical AI systems.

This read-only MCP Server allows you to connect to Google BigQuery data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

MCP server for the Agent Platform API, enabling management of sandbox environments through MCP protocol. Exposes tools to interact with sandbox lifecycle operations such as start, stop, and status checks.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

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.

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.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

An MCP server that enables AI applications to access 20+ model providers (including OpenAI, Anthropic, Google) through a unified interface for text and image generation.

Enables Gemini-powered multimodal analysis (video, audio, image, documents), Google search, and code execution via the API易 service. Supports Docker deployment and flexible configuration.

Remote-control Unity Editor from any AI IDE via file-based IPC — 62 tools, zero dependencies

Bridges Claude Desktop to local Windows 10 development environments to enable repository reading, documentation searching, and isolated code execution. It provides a secure interface for running allowlisted terminal commands and scripts directly through natural language.

Runs AI-generated code in secure Firecracker microVMs with opt-in network policy enforcement, PII scanning, prompt injection defense, and audit logging. Exposes MCP tools for running commands, managing files, and the full sandbox lifecycle.

MCP for Scorable Evaluation Platform

Enables AI agents to interact with the Execute.run bot API for managing Shell balances, transferring funds, and executing LLM requests. It provides tools for identity verification, transaction tracking, and performing compute tasks through the Execute.run platform.

The first AI-powered development tool for Ignition SCADA — an MCP server that lets any AI agent read, understand, and interact with your Ignition projects and gateways.

A Model Context Protocol server that connects to Google AI Studio/Gemini API, enabling content generation with support for various file types, conversation history, and system prompts.

Enables AI models to dynamically create and execute their own custom tools through a meta-function architecture, supporting JavaScript, Python, and Shell runtimes with sandboxed security and human approval flows.

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 powerful AI-driven tool that helps developers create beautiful, modern UI components instantly through natural language descriptions.

Enables AI agents to program MicroPython boards such as Raspberry Pi Pico and ESP32 through Thonny, with visible shell commands and optional human approval.

The Problem Modern distributed systems and AI agents require robust operational scaffolding. Simple CRUD apps or mock loops fail when subjected to real-world edge cases, asynchronous boundaries, and security constraints. The Solution An isolated execution platform for AI agents. An agent is given a broken service and a bounded toolset inside a disposable sandbox. Every command passes throug

MCP server that delegates code writing, editing, and execution to OpenCode, allowing AI assistants to focus on decision-making.

Enables AI agents to observe and control a browser-based 2D robot simulator, including driving, configuring sensors and modules, running programs, and generating wiring, firmware, and parts lists for ESP32, Arduino Uno, or Raspberry Pi.

Enables deterministic verification for AI assistants by executing Python code that uses symbolic engines like SymPy and Z3 for math, logic, and code analysis.

Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.

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 AI assistants to build, test, run, and manage Apple platform projects (iOS, macOS, tvOS, watchOS, visionOS) directly through Xcode. Provides comprehensive control over Xcode projects, Swift packages, simulators, and development workflows without leaving your editor.

Provides web search and code execution capabilities using OpenAI models, with optional code interpreter and conversation continuity via response IDs.