InfoWorld called official maintenance the real advantage. The catalog is broad and it keeps moving. You pay for that with more moving parts. There is not one “the AWS MCP server” in the way Microsoft presents Azure MCP Server. You choose from a suite, or you point at the managed endpoint.
Match the server to the job, not to a brand preference. An ops agent that should list buckets and rotate keys belongs on AWS’s API path or on Azure MCP, depending on the account. An analyst agent that should run constrained SQL belongs on MCP Toolbox. A Copilot Studio bot that should call an internal tool belongs on a remote HTTPS MCP server registered in Studio, which can sit in Azure even if the tool is not an Azure resource.
AWS: a catalog, then a managed remote server
BigQuery has a documented path to pre-built MCP tools. Gemini CLI extensions and Gemini Code Assist can bundle the server, so you may not install Toolbox separately if you stay inside those clients. Google’s MCP overview treats local versus remote and managed versus self-hosted as first-class choices.
Where it runs. AWS now offers local open-source servers and a hosted AWS MCP Server. Azure offers the developer-facing Azure MCP Server and a hosted Foundry MCP Server. Google’s Toolbox is something you run, often next to the IDE, with Gemini products bundling it. Hosted servers cut ops. Local servers keep credentials and data on your machine.
The trade is obvious. An agent that should run constrained SQL against AlloyDB or BigQuery is on home ground. An agent that should provision clusters or rewrite IAM the way AWS’s API path can mutate AWS resources is a different job. Google’s MCP story is strongest where the data lives.
Azure: one productized server, Entra ID, Copilot as the client
That open-source API server is already being folded into something hosted. The AWS MCP Server (in preview), described in the same Labs repo, is remote and run by AWS. It pairs API access with documentation retrieved at query time, Getting Started material, and pre-built agent SOPs. Calls are syntactically validated. Activity can land in CloudWatch and CloudTrail. OAuth 2.0 is part of the design. The point is not a clever protocol wrapper. It is putting the agent on the same control plane you already use: IAM for permission, CloudTrail for the record.
If you are connecting an agent to cloud resources, picking an MCP server is picking what the model can see and who authenticates the call.
The interesting split is not which cloud “has MCP.” They all do. It is whether you want a general cloud control plane for an agent (AWS and Azure) or a guarded door into data (Google), and whether that door is hosted by the vendor or sitting next to the model on your laptop.
Google Cloud: databases first, Gemini as the usual client
Google did not start with a kitchen-sink control plane. It started with data. MCP Toolbox for Databases (formerly Gen AI Toolbox for Databases) is an open-source MCP server that connects agents to enterprise databases. Google later said it natively supports 42 data sources, including AlloyDB, Cloud SQL, Cloud Spanner, and third-party stores. The Toolbox takes on connection pooling, authentication, and observability so that glue is not rewritten for every agent.
Multi-cloud agents will run more than one MCP server. That is normal. The protocol is shared. The servers are still product-specific, with product-specific blast radius.
What the agent can touch. AWS is APIs and docs across many services. Azure is Azure resources plus developer CLIs plus Foundry and Copilot. Google is databases. Community servers still fill gaps (GitHub, Slack, Postgres, Drive) that none of the three treat as their main job.
Auth, hosting, and surface area
Three dimensions matter more than the logo.
AWS Labs publishes open-source MCP servers as a monorepo. The set is meant to sit in a developer workflow: documentation lookup, API access, and specialized servers for individual services. In July 2025 AWS announced the AWS API MCP Server, which lets an MCP client discover supported AWS APIs and inspect, create, or change resources through the model. Access control is IAM.
Docs are the other difference. InfoWorld found Azure’s getting-started path easier than AWS’s, with clearer notes on tool parameters and on turning off agent control of sensitive functions. If the shop already lives in Entra and Copilot, this is the least awkward fit. The scope is Azure-shaped: resources, CLIs, Foundry, Copilot. It is not a general database gateway.
Microsoft’s Azure MCP Server is the named entry. It speaks MCP to clients such as GitHub Copilot agent mode, the OpenAI Agents SDK, and Semantic Kernel. Sign-in is Entra ID through the Azure Identity library. The server covers Azure resources and also the Azure CLI and Azure Developer CLI (azd), which matters if the agent is supposed to do what a person would do at a terminal.
How to choose without a bake-off
Who signs in. AWS leans on IAM, and on OAuth for the managed server. Azure leans on Entra ID. Google’s Toolbox is built around database credentials and Google Cloud auth for those sources. Mixing clouds means mixing identity models. An agent with three MCP servers is three trust boundaries.
There is a hosted track as well. Foundry MCP Server (preview) is cloud-hosted. You do not stand up infrastructure. Agents get a curated set of read and write tools against Foundry services, still authenticated with Entra ID. Copilot Studio can register an MCP server as an HTTPS endpoint so a studio-built agent discovers tools at runtime, as Microsoft Q&A describes for custom servers running in Azure.
The Model Context Protocol is the common wire between an AI agent and the tools it is allowed to use. AWS, Azure, and Google Cloud all ship MCP servers now, and they are not the same product with different logos. One side of the market is a cloud control plane for agents. The other is a guarded door into data. Identity, hosting, and blast radius follow from that split.
