"Deep research on Gemini" matching MCP servers:

Connects MCP clients to Gemini Cloud Assist APIs, enabling users to understand, manage, and troubleshoot their Google Cloud environment using natural language.

Orchestrates distributed research drones on Google Cloud Run, enabling scalable and bidirectional research task management.

Enables LLMs to securely query and optionally write to ServiceNow instances with strict allowlist/denylist safety controls.

An MCP server that enables LLMs to search, fetch, and act on Google Workspace (Drive, Gmail, Docs, Sheets, etc.) with rich, one-call results and file deposits to disk, reducing context usage.

Enables AI coding agents to deploy and manage full multi-service applications on Constructor from a single request, covering image builds, service configuration, start/stop lifecycle, logs, and file syncing on research desks. It handles browser-based sign-in and token renewal automatically so agents can inspect deployments and report results without manual setup.

Enables declarative FDTD validation, guarded cloud-task workflows, and normalization of Tidy3D simulation results into metadata and bounded CSV observables, while keeping credentials and cloud-execution privileges isolated.

Implementation of Model Context Protocol (MCP) server that provides tools for accessing Google Cloud's Vertex AI Gemini models, supporting features like web search grounding and direct knowledge answering for coding assistance and general queries.

A server providing a unified interface to interact with Firebase services, including Authentication, Firestore, and Storage.

Enables interaction with Google Docs through a FastMCP server, allowing users to list, read, and create documents. It leverages the Google Docs and Drive APIs to provide seamless document management via Gemini-CLI.

A server that enables Model Context Protocol (MCP) functionality on Cloudflare Workers with OAuth login, allowing applications like Claude Desktop to access custom tools through a standardized interface.

A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.

Provides tools for discovering, building, deploying, and troubleshooting AWS Deep Learning Containers (DLC) images. Supports multiple frameworks and instance recommendations.

A template for deploying an MCP server on Vercel, with example tools like roll_dice and get_weather.

这是一个基于 ALAPI 的 MCP (Model Control Protocol) 服务器实现，可以通过MCP协议直接调用ALAPI的接口

Enables deploying and connecting to MCP servers on Cloudflare Workers with OAuth login, allowing remote access to tools via MCP clients like Claude Desktop.

Lightweight MCP server that allows agents to interface with the Neon REST API, deployed on Cloudflare Workers.

Enables AI-powered image and video analysis using Google Gemini and Vertex AI models. Supports analyzing single or multiple images, detecting objects with bounding boxes, and video content analysis through natural language prompts.

Enables deep integration with Google Workspace for AI assistants, allowing for document creation, spreadsheet manipulation, and file management. It supports advanced features like template processing, comment thread management, and cell formatting.

A FastMCP-based server that provides tools for retrieving animal species lists and specific animal details from a fictional zoo. It serves as a reference for deploying and securing Model Context Protocol servers on Google Cloud Run.

Deploys a Remote MCP server bridge for Plaud on Google Cloud Run, enabling Gemini Enterprise apps to connect via Dynamic Client Registration and OAuth 2.0 proxy.

Enables AI assistants such as Claude, Gemini, and GPT to connect directly to VMware ESXi and vCenter hosts over stdio JSON-RPC. Provides full virtual machine lifecycle management (create, clone, delete, power on/off) plus real-time CPU, memory, storage, and network performance monitoring, with API key authentication and SSL/TLS support.

Enables mathematical operations such as addition, subtraction, multiplication, and division through natural language, deployed on Google Kubernetes Engine with secure and scalable infrastructure.

Enables management of GCP resources and integration with AI development tools via a stateless MCP server hosted on Cloud Run.

Enables running a stateless Model Context Protocol server over Streamable HTTP on Cloudflare Workers or any fetch-capable runtime, with signed sessions, origin checks, and JSON-RPC request handling. It lets developers define tools with JSON Schema validation and per-request context.

Provides a standardized interface for interacting with Google-Gemini's tools and services through a unified API.

Enables deployment of an MCP server on Cloudflare Workers with OAuth login, allowing remote invocation of tools like math operations from MCP clients.

A FastMCP-powered Model Context Protocol server for Google Ads API integration with automatic OAuth 2.0 authentication Connect Google Ads API directly to MCP Clients with seamless OAuth 2.0 authentication, automatic token refresh, GAQL querying, and keyword research capabilities.

Model Context Protocol (MCP) Server for the mcp API.

Enables Gemini Enterprise to connect to Cloudflare Zero Trust MCP server portals via Dynamic Client Registration (DCR) and OAuth 2.0 proxying. It proxies MCP JSON-RPC/SSE traffic with Cloudflare Access service token authentication.

A deployable Model Context Protocol server that runs on Cloudflare Workers without requiring authentication, allowing users to create custom AI tools and connect them to clients like Claude Desktop and Cloudflare AI Playground.