AWS MCP Servers vs Azure MCP Server

When to pick AWS MCP Servers

• You are writing CloudFormation or CDK infrastructure and need AWS-specific guidance. The AWS IaC server brings together CloudFormation documentation, CDK construct examples, security validation and deployment troubleshooting. Choose that package when infrastructure code is the task you want your assistant to handle.
• Your application runs on EKS, ECS or Lambda. The collection includes separate servers for Kubernetes cluster management, ECS deployment and Lambda invocation. You can choose the server around the operation, such as deploying a container application or exposing existing Lambda functions as tools.
• You need AWS documentation or information from a Bedrock knowledge base. The Documentation server retrieves AWS documentation and API references; the Bedrock Knowledge Bases Retrieval server queries enterprise knowledge bases with citation support. These are separate packages with different purposes.
• You are investigating AWS operations or planning infrastructure expenses. CloudWatch has a dedicated server for metrics, alarms and logs, while the Pricing server supports service pricing and cost estimates. Add the packages that match the investigation or planning work.

When to pick Azure MCP Server

• Your application uses several Azure services, such as Storage, Cosmos DB and Key Vault, and you want their tools under one server entry. Azure groups service tools into namespaces within a single process, so you select service coverage inside that server.
• Your work involves Azure Monitor, Kusto or AKS and you want those service namespaces available to your coding assistant. They belong to the same server alongside application services such as App Configuration, allowing you to configure the service mix around your project.
• Your development machine already has an Azure sign-in that you want to reuse. The server uses existing Azure authentication instead of requiring a separate server key. This fits a workflow where developers already access Azure from their machines.
• You want to expose only selected Azure service groups to the model. Namespace filtering narrows the available tool list while retaining the same server process. Pick this structure when changing the exposed service set is preferable to configuring separate service packages.

Main differences

• Package boundaries differ. AWS distributes independent servers for particular services or workflows; Azure collects its tools in one server. With AWS, you choose packages and client entries. With Azure, you choose namespaces within the shared process.
• Both documented command-based setups run locally and communicate with the client over stdio. The AWS README also lists managed remote services, but those have their own connection details. Their hosting and authentication properties should not be attributed to the local AWS packages compared here.
• Account access follows each cloud's existing authentication setup. AWS account-facing servers read standard credential settings such as AWS_PROFILE. Azure reuses the machine's Azure sign-in and does not ask for a separate key for the MCP server.
• Launch options differ. AWS publishes Python packages selected by name in a uvx command. Azure uses .NET and offers launch paths through npx, dnx, uvx or Docker. Using uvx for Azure still starts the Azure server, rather than selecting an AWS-style service package.
• Documentation access has different packaging. AWS includes a dedicated Documentation server in its collection. Microsoft Learn is a separate, hosted documentation endpoint that needs no account; it is distinct from the Azure MCP Server used to access Azure services.

Setup

For AWS, install uv and Python, then configure a stdio client entry using uvx awslabs.aws-documentation-mcp-server@latest. This starts the Documentation server. For account operations, choose the appropriate package and configure credentials with access to the required services, including AWS_PROFILE where applicable. Check that server's requirements and options.

For Azure, configure a stdio client entry using npx -y @azure/mcp@latest server start. The server reuses the machine's existing Azure sign-in. Use namespace filtering to limit the exposed service tools to your project's needs; the Microsoft Learn documentation endpoint requires a separate connection.