AWS Data Processing MCP Server

What is AWS Data Processing MCP Server?

AWS Data Processing MCP Server is an MCP-compatible server that exposes AWS data processing capabilities, especially for services like AWS Glue and Amazon EMR-EC2, to LLM-powered agents and developer tools. By integrating with this server, your AI assistants or autonomous agents can perform data pipeline orchestration, ETL (Extract, Transform, Load) operations, job monitoring, troubleshooting, and automation within your existing AWS environment. This bridges the gap between AI-driven development and hands-on cloud data engineering, making it easy for teams to automate, optimize, and scale their data workflows.

How to Configure

1. Prerequisites: Ensure you have uv installed, Python available (e.g., uv python install 3.10), and your AWS credentials set up for services you wish to manage.
2. Add to MCP Client: Add the AWS Data Processing MCP Server to your MCP client configuration file (e.g., mcp.json, cline_mcp_settings.json, or ~/.codeium/windsurf/mcp_config.json), specifying the command, arguments, and required environment variables.

   {
     "awslabs.aws-dataprocessing-mcp-server": {
       "command": "uvx",
       "args": ["awslabs.aws-dataprocessing-mcp-server@latest"],
       "env": {
         "AWS_PROFILE": "your-aws-profile",
         "AWS_REGION": "us-east-1",
         "FASTMCP_LOG_LEVEL": "ERROR"
       }
     }
   }
   

3. Grant Permissions: Make sure your AWS profile has the necessary IAM permissions to interact with Glue, EMR, and other required services.
4. Container Usage: Optionally, run it in a Docker container, using --env-file and mounting your AWS credentials for secure execution.
5. Verification: Validate the server setup by running a basic command or viewing available tools from your MCP client.

How to Use

• In your coding assistant (e.g., Cline, Cursor, Claude, or Windsurf), ensure the AWS Data Processing MCP Server is enabled.
• From the AI chat or command interface, prompt actions such as:
  • "List all Glue data pipelines."
  • "Start an EMR job to process this dataset."
  • "Monitor this ETL workflow for errors."
  • "What tables are currently registered in Glue Catalog?"
• The AI agent will leverage the MCP server’s tools (functions) to execute these operations, display progress, and return results.
• For automation, background processes, or workflow agents, automate data jobs by orchestrating server tool calls in code.
• Use the server’s endpoints, such as tools/list for available operations and tools/call for direct task invocation.

Key Features

• Real-time Pipeline Visibility: Monitor, debug, and analyze AWS Glue and EMR job runs and pipelines.
• Programmatic Workflow Orchestration: Start, stop, and manage data processing jobs using simple tool invocations.
• Unified LLM Integration: Bring AWS data operations into any LLM-powered agent, chat interface, or workflow automation tool.
• Comprehensive ETL Support: Create, update, and schedule ETL workflows across your data lakes and cloud environments.
• Secure Operations: Connects through your AWS credentials and enforces your IAM policies.
• Error Reporting and Troubleshooting: Fetch logs, status, and error details for faster diagnostics and response.
• Tool Discovery and Documentation: Easily list available operations and get tool descriptions within your MCP client.

Use Cases

• AI-Driven Data Engineering: Automatically generate, manage, or troubleshoot Glue/EMR jobs from language model prompts.
• Pipeline Monitoring and Alerting: Observe workflow status, fetch job outputs, and trigger Slack/email notifications on failure.
• Conversational DataOps: Ask natural language questions about your data pipelines, recent job statuses, or historical runs and get instant structured answers.
• Automated ETL Orchestration: Schedule, update, and coordinate multi-step ETL workflows using LLM-controlled agents.
• Data Catalog Exploration: List available tables, partitions, and schemas from the Glue Data Catalog for analytics or compliance tasks.
• Self-Serve Data Pipelines: Empower less-technical users to perform data operations through natural language interfaces integrating the MCP server.
• Headless Background Agents: Implement autonomous agents for routine data pipeline health checks, automated restarts, and incremental error remediation.

FAQ

1. What AWS services does this MCP server support?

The AWS Data Processing MCP Server primarily supports AWS Glue and Amazon EMR-EC2, but may also integrate with related data services such as Glue Data Catalog. Check the server's available tools for a complete list.

2. How does authentication and access control work?

The server uses your configured AWS credentials and region. Your operations are governed by the IAM policies of your AWS profile, ensuring actions comply with your security standards.

3. Can I use this server to trigger, monitor, and stop jobs from an AI chat interface?

Yes! The exposed tools allow you to start, stop, monitor, or query both Glue and EMR jobs in real-time, directly from AI coding assistants or workflows.

4. Is it possible to run the server inside a container?

Absolutely. The server can be containerized (e.g., with Docker), mounting your .aws credentials and passing environment variables with the --env-file and --volume options.

5. How do I troubleshoot if my jobs are not running as expected?

You can use available tools to fetch job logs, status, and errors. Additionally, make sure your IAM permissions include all relevant Glue, EMR, and logging APIs.