awslabs.sagemaker-ai-mcp-server 1.0.0

MCP server for AWS SageMaker AI

Amazon SageMaker AI MCP Server

The Amazon SageMaker AI MCP server provides agents with tools to enable high-performance, low-cost AI/ML model development. Currently, this server includes tools for managing SageMaker HyperPod clusters.

Available Features

SageMaker HyperPod

Provides comprehensive tools for managing SageMaker HyperPod clusters orchestrated with Amazon EKS or Slurm, including cluster deployment, node management, and lifecycle operations. See the HyperPod documentation for detailed information on the supported tools.

Prerequisites

• Install Python 3.10+
• Install the uv package manager
• Install and configure the AWS CLI with credentials

Quickstart

This quickstart guide walks you through the steps to configure the Amazon SageMaker AI MCP Server for use with both the Cursor IDE and the Amazon Q Developer CLI.

Set up Cursor

Cursor	VS Code

Set up the Amazon Q Developer CLI

1. Install the Amazon Q Developer CLI.
2. The Q Developer CLI supports MCP servers for tools and prompts out-of-the-box. Edit your Q developer CLI's MCP configuration file named mcp.json following these instructions.

The example below includes both the --allow-write flag for mutating operations and the --allow-sensitive-data-access flag for accessing logs and events:

For Mac/Linux:

```
{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "awslabs.sagemaker-ai-mcp-server@latest",
        "--allow-write",
        "--allow-sensitive-data-access"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "autoApprove": [],
      "disabled": false
    }
  }
}
```

For Windows:

```
{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "awslabs.sagemaker-ai-mcp-server@latest",
        "awslabs.sagemaker-ai-mcp-server.exe",
        "--allow-write",
        "--allow-sensitive-data-access"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "autoApprove": [],
      "disabled": false
    }
  }
}
```

3. Verify your setup by running the /tools command in the Q Developer CLI to see the available SageMaker AI MCP tools.

Note that this is a basic quickstart. We recommend to use SageMaker AI MCP server in conjunction with AWS API MCP Server, AWS Knowledge MCP Server/AWS Documentation MCP Server, and AWS EKS MCP Server to gain complete coverage for all SageMaker APIs and effectively troubleshoot common issues.

Configurations

Arguments

The args field in the MCP server definition specifies the command-line arguments passed to the server when it starts. These arguments control how the server is executed and configured. For example:

For Mac/Linux:

{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "awslabs.sagemaker-ai-mcp-server@latest",
        "--allow-write",
        "--allow-sensitive-data-access"
      ],
      "env": {
        "AWS_PROFILE": "your-profile",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

For Windows:

{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "awslabs.sagemaker-ai-mcp-server@latest",
        "awslabs.sagemaker-ai-mcp-server.exe",
        "--allow-write",
        "--allow-sensitive-data-access"
      ],
      "env": {
        "AWS_PROFILE": "your-profile",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

Command Format

The command format differs between operating systems:

For Mac/Linux:

• awslabs.sagemaker-ai-mcp-server@latest - Specifies the latest package/version specifier for the MCP client config.

For Windows:

• --from awslabs.sagemaker-ai-mcp-server@latest awslabs.sagemaker-ai-mcp-server.exe - Windows requires the --from flag to specify the package and the .exe extension.

--allow-write (optional)

Enables write access mode, which allows mutating operations (e.g., create, update, delete resources).

• Default: true (The server runs in write mode by default)
• Example: remove --allow-write from the args list in your MCP server definition to switch to readonly mode.

--allow-sensitive-data-access (optional)

Enables access to sensitive data such as logs, events, and resource details. This flag is required for tools that access potentially sensitive information.

• Default: true (Access to sensitive data is allowed by default)
• Example: remove --allow-sensitive-data-access from the args list in your MCP server definition to disable it.

Environment variables

The env field in the MCP server definition allows you to configure environment variables that control the behavior of the SageMaker AI MCP server. For example:

{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "AWS_PROFILE": "my-profile",
        "AWS_REGION": "us-west-2"
      }
    }
  }
}

FASTMCP_LOG_LEVEL (optional)

Sets the logging level verbosity for the server.

• Valid values: "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"
• Default: "WARNING"
• Example: "FASTMCP_LOG_LEVEL": "ERROR"

AWS_PROFILE (optional)

Specifies the AWS profile to use for authentication.

• Default: None (If not set, uses default AWS credentials).
• Example: "AWS_PROFILE": "my-profile"

AWS_REGION (optional)

Specifies the AWS region where SageMaker resources are managed, which will be used for all AWS service operations.

• Default: None (If not set, uses default AWS region).
• Example: "AWS_REGION": "us-west-2"

Security & Permissions

Features

The SageMaker AI MCP Server implements the following security features:

1. AWS Authentication: Uses AWS credentials from the environment for secure authentication.
2. SSL Verification: Enforces SSL verification for all AWS API calls.
3. Resource Tagging: Tags all created resources for traceability.
4. Least Privilege: Uses IAM roles with appropriate permissions.
5. Stack Protection: Ensures CloudFormation stacks for HyperPod can only be modified by the tool that created them.

Considerations

When using the SageMaker AI MCP Server, consider the following:

• AWS Credentials: The server needs permission to create and manage SageMaker AI resources.
• Network Security: Configure VPC and security groups properly for SageMaker AI resources.
• Authentication: Use appropriate authentication mechanisms for AWS resources.
• Authorization: Configure IAM properly for AWS resources.
• Data Protection: Encrypt sensitive data in SageMaker AI resources.
• Logging and Monitoring: Enable logging and monitoring for SageMaker AI resources.

Permissions

The SageMaker AI MCP Server can be used for production environments with proper security controls in place. The server runs in read-only mode by default, which is recommended and considered generally safer for production environments. Only explicitly enable write access when necessary. Below are the HyperPod MCP tools available in read-only versus write-access mode:

• Read-only mode (default): manage_hyperpod_stacks (with operation="describe"), manage_hyperpod_cluster_nodes (with operations="list_clusters", "list_nodes", "describe_node").
• Write-access mode: (require --allow-write): manage_hyperpod_stacks (with "deploy", "delete"), manage_hyperpod_cluster_nodes (with operations="update_software", "batch_delete").

autoApprove (optional)

An array within the MCP server definition that lists tool names to be automatically approved by the MCP Server client, bypassing user confirmation for those specific tools. For example:

For Mac/Linux:

{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "awslabs.sagemaker-ai-mcp-server@latest"
      ],
      "env": {
        "AWS_PROFILE": "sagemaker-ai-mcp-readonly-profile",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "INFO"
      },
      "autoApprove": [
        "manage_hyperpod_stacks",
        "manage_hyperpod_cluster_nodes"
      ]
    }
  }
}

For Windows:

{
  "mcpServers": {
    "awslabs.sagemaker-ai-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "awslabs.sagemaker-ai-mcp-server@latest",
        "awslabs.sagemaker-ai-mcp-server.exe"
      ],
      "env": {
        "AWS_PROFILE": "sagemaker-ai-mcp-readonly-profile",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "INFO"
      },
      "autoApprove": [
        "manage_hyperpod_stacks",
        "manage_hyperpod_cluster_nodes"
      ]
    }
  }
}

Role Scoping Recommendations

In accordance with security best practices, we recommend the following:

1. Create dedicated IAM roles to be used by the SageMaker AI MCP Server with the principle of "least privilege."
2. Use separate roles for read-only and write operations.
3. Implement resource tagging to limit actions to resources created by the server.
4. Enable AWS CloudTrail to audit all API calls made by the server.
5. Regularly review the permissions granted to the server's IAM role.
6. Use IAM Access Analyzer to identify unused permissions that can be removed.

Sensitive Information Handling

IMPORTANT: Do not pass secrets or sensitive information via allowed input mechanisms:

• Do not include secrets or credentials in CloudFormation templates.
• Do not pass sensitive information directly in the prompt to the model.
• Avoid using MCP tools for creating secrets, as this would require providing the secret data to the model.

CloudFormation Template Security:

• Only use CloudFormation templates from trustworthy sources.
• The server relies on CloudFormation API validation for template content and does not perform its own validation.
• Audit CloudFormation templates before applying them to your cluster.

Instead of passing secrets through MCP:

• Use AWS Secrets Manager or Parameter Store to store sensitive information.
• Configure proper IAM roles for service accounts.
• Use IAM roles for service accounts (IRSA) for AWS service access.

File System Access and Operating Mode

Important: This MCP server is intended for STDIO mode only as a local server using a single user's credentials. The server runs with the same permissions as the user who started it and has complete access to the file system.

Security and Access Considerations

• Full File System Access: The server can read from and write to any location on the file system where the user has permissions
• Host File System Sharing: When using this server, the host file system is directly accessible
• Do Not Modify for Network Use: This server is designed for local STDIO use only; network operation introduces additional security risks

Common File Operations

The MCP server can create a templated params json file to a user-specified absolute file path during hyperpod cluster creation.

General Best Practices

• Resource Naming: Use descriptive names for SageMaker AI resources.
• Error Handling: Check for errors in tool responses and handle them appropriately.
• Resource Cleanup: Delete unused resources to avoid unnecessary costs.
• Monitoring: Monitor resource status regularly.
• Security: Follow AWS security best practices for SageMaker AI resources.
• Backup: Regularly backup important SageMaker AI resources.

General Troubleshooting

• Permission Errors: Verify that your AWS credentials have the necessary permissions.
• CloudFormation Errors: Check the CloudFormation console for stack creation errors.
• SageMaker API Errors: Verify that the HyperPod cluster is running and accessible.
• Network Issues: Check VPC and security group configurations.
• Client Errors: Verify that the MCP client is configured correctly.
• Log Level: Increase the log level to DEBUG for more detailed logs.

For service-specific issues, consult the relevant service documentation:

• HyperPod Documentation
• Amazon SageMaker AI Documentation

Version

Current MCP server version: 1.0.0