Setup for Troubleshooting Agent - Amazon EMR
PrerequisitesSetup Resources

Setup for Troubleshooting Agent

Note

The Apache Spark Troubleshooting Agent uses cross-region inference to process natural language requests and generate responses. For more details, please refer to Cross-Region Processing for the Apache Spark Troubleshooting Agent. The Amazon SageMaker Unified Studio MCP server is in preview and is subject to change.

Prerequisites

Before we begin our setup process for integration with Kiro CLI, make sure you have the following installed on your workstation:

Setup Resources

You can use an AWS CloudFormation template to setup the resource for the MCP server. These templates are samples that you should modify to meet your requirements. The template creates the following resources for the troubleshooting process:

  1. IAM role which has permissions to call MCP Server and required permissions for troubleshooting process for the selected platform.

Choose one of the Launch Stack buttons in the following table. This launches the stack on the AWS CloudFormation console in the respective region.

Region Launch
US East (Ohio)
US East (N. Virginia)
US West (Oregon)
Asia Pacific (Tokyo)
Europe (Ireland)
Asia Pacific (Singapore)
Asia Pacific (Sydney)
Canada (Central)
South America (São Paulo)
Europe (Frankfurt)
Europe (Stockholm)
Europe (London)
Europe (Paris)
Asia Pacific (Seoul)
Asia Pacific (Mumbai)

Proceed to the Specify stack details page, enter the Stack name. Enter additional information under Parameters. Provide the following information and proceed to create the stack.

  • TroubleshootingRoleName - Name of the IAM role to create for troubleshooting operations

  • EnableEMREC2 - Enable EMR-EC2 troubleshooting permissions (default: true)

  • EnableEMRServerless - Enable EMR-Serverless troubleshooting permissions (default: true)

  • EnableGlue - Enable Glue troubleshooting permissions (default: true)

  • CloudWatchKmsKeyArn - (Optional) ARN of existing KMS key for CloudWatch Logs encryption (EMR Serverless only, leave empty for default encryption)

Open the Outputs tab and copy the 1-line instruction from the CloudFormation output to set your environment variables and execute it in your local environment. Example 1-line instruction:

export SMUS_MCP_REGION=us-east-1 && export IAM_ROLE=arn:aws:iam::111122223333:role/spark-troubleshooting-role-xxxxxx

Then run the following command locally to setup the IAM profile and MCP server configuration:

# Step 1: Configure AWS CLI Profile
aws configure set profile.smus-mcp-profile.role_arn ${IAM_ROLE}
aws configure set profile.smus-mcp-profile.source_profile <AWS CLI Profile to assume the IAM role - ex: default>
aws configure set profile.smus-mcp-profile.region ${SMUS_MCP_REGION}

# Step 2: if you are using kiro CLI, use the following command to add the MCP configuration
# Add Spark Troubleshooting MCP Server
kiro-cli-chat mcp add \
    --name "sagemaker-unified-studio-mcp-troubleshooting" \
    --command "uvx" \
    --args "[\"mcp-proxy-for-aws@latest\",\"https://sagemaker-unified-studio-mcp.${SMUS_MCP_REGION}.api.aws/spark-troubleshooting/mcp\", \"--service\", \"sagemaker-unified-studio-mcp\", \"--profile\", \"smus-mcp-profile\", \"--region\", \"${SMUS_MCP_REGION}\", \"--read-timeout\", \"180\"]" \
    --timeout 180000 \
    --scope global
    
# Add Spark Code Recommendation MCP Server
kiro-cli-chat mcp add \
    --name "sagemaker-unified-studio-mcp-code-rec" \
    --command "uvx" \
    --args "[\"mcp-proxy-for-aws@latest\",\"https://sagemaker-unified-studio-mcp.${SMUS_MCP_REGION}.api.aws/spark-code-recommendation/mcp\", \"--service\", \"sagemaker-unified-studio-mcp\", \"--profile\", \"smus-mcp-profile\", \"--region\", \"${SMUS_MCP_REGION}\", \"--read-timeout\", \"180\"]" \
    --timeout 180000 \
    --scope global

This should update ~/.kiro/settings/mcp.json to include the MCP server configuration as below.

{
  "mcpServers": {
    "sagemaker-unified-studio-mcp-troubleshooting": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-proxy-for-aws@latest",
        "https://sagemaker-unified-studio-mcp.us-east-1.api.aws/spark-troubleshooting/mcp",
        "--service",
        "sagemaker-unified-studio-mcp",
        "--profile",
        "smus-mcp-profile",
        "--region",
        "us-east-1",
        "--read-timeout",
        "180"
      ],
      "timeout": 180000,
      "disabled": false
    },
    "sagemaker-unified-studio-mcp-code-rec": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mcp-proxy-for-aws@latest",
        "https://sagemaker-unified-studio-mcp.us-east-1.api.aws/spark-code-recommendation/mcp",
        "--service",
        "sagemaker-unified-studio-mcp",
        "--profile",
        "smus-mcp-profile",
        "--region",
        "us-east-1",
        "--read-timeout",
        "180"
      ],
      "timeout": 180000,
      "disabled": false
    }
  }
}

See Supported Interfaces for the configuration guidance for different MCP clients like Kiro, Cline and GitHub CoPilot.