awslabs.aws-bedrock-data-automation-mcp-server 0.0.10

An AWS Labs Model Context Protocol (MCP) server for Bedrock Data Automation

AWS Bedrock Data Automation MCP Server

A Model Context Protocol (MCP) server for Amazon Bedrock Data Automation that enables AI assistants to analyze documents, images, videos, and audio files using Amazon Bedrock Data Automation projects.

Features

• Project Management: List and get details about Bedrock Data Automation projects
• Asset Analysis: Extract insights from unstructured content using Bedrock Data Automation
• Support for Multiple Content Types: Process documents, images, videos, and audio files
• Integration with Amazon S3: Seamlessly upload and download assets and results

Prerequisites

1. Install uv from Astral or the GitHub README
2. Install Python using uv python install 3.10
3. Set up AWS credentials with access to Amazon Bedrock Data Automation
   • You need an AWS account with Amazon Bedrock Data Automation enabled
   • Configure AWS credentials with aws configure or environment variables
   • Ensure your IAM role/user has permissions to use Amazon Bedrock Data Automation
4. Create an AWS S3 Bucket
   • Example AWS CLI command to create the bucket

   •  aws s3 create-bucket <bucket-name>
     

Installation

Cursor	VS Code

Configure the MCP server in your MCP client configuration (e.g., for Amazon Q Developer CLI, edit ~/.aws/amazonq/mcp.json):

{
  "mcpServers": {
    "bedrock-data-automation-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.aws-bedrock-data-automation-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1",
        "AWS_BUCKET_NAME": "your-s3-bucket-name",
        "BASE_DIR": "/path/to/base/directory",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Windows Installation

For Windows users, the MCP server configuration format is slightly different:

{
  "mcpServers": {
    "awslabs.aws-bedrock-data-automation-mcp-server": {
      "disabled": false,
      "timeout": 60,
      "type": "stdio",
      "command": "uv",
      "args": [
        "tool",
        "run",
        "--from",
        "awslabs.aws-bedrock-data-automation-mcp-server@latest",
        "awslabs.aws-bedrock-data-automation-mcp-server.exe"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

or docker after a successful docker build -t awslabs/aws-bedrock-data-automation-mcp-server .:

# fictitious `.env` file with AWS temporary credentials
AWS_ACCESS_KEY_ID=<from the profile you set up>
AWS_SECRET_ACCESS_KEY=<from the profile you set up>
AWS_SESSION_TOKEN=<from the profile you set up>
AWS_REGION=<your-region>
AWS_BUCKET_NAME=<your-s3-bucket-name>
BASE_DIR=/path/to/base/directory

{
  "mcpServers": {
    "bedrock-data-automation-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "--interactive",
        "--env-file",
        "/full/path/to/file/above/.env",
        "awslabs/aws-bedrock-data-automation-mcp-server:latest"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}

NOTE: Your credentials will need to be kept refreshed from your host

Environment Variables

• AWS_PROFILE: AWS CLI profile to use for credentials
• AWS_REGION: AWS region to use (default: us-east-1)
• AWS_BUCKET_NAME: S3 bucket name for storing assets and results
• BASE_DIR: Base directory for file operations (optional)
• FASTMCP_LOG_LEVEL: Logging level (ERROR, WARNING, INFO, DEBUG)

AWS Authentication

The server uses the AWS profile specified in the AWS_PROFILE environment variable. If not provided, it defaults to the default credential provider chain.

"env": {
  "AWS_PROFILE": "your-aws-profile",
  "AWS_REGION": "us-east-1"
}

Make sure the AWS profile has permissions to access Amazon Bedrock Data Automation services. The MCP server creates a boto3 session using the specified profile to authenticate with AWS services. Amazon Bedrock Data Automation services is currently available in the following regions: us-east-1 and us-west-2.

Tools

getprojects

Get a list of data automation projects.

getprojects() -> list

Returns a list of available Bedrock Data Automation projects.

getprojectdetails

Get details of a specific data automation project.

getprojectdetails(projectArn: str) -> dict

Returns detailed information about a specific Bedrock Data Automation project.

analyzeasset

Analyze an asset using a data automation project.

analyzeasset(assetPath: str, projectArn: Optional[str] = None) -> dict

Extracts insights from unstructured content (documents, images, videos, audio) using Amazon Bedrock Data Automation.

• assetPath: Path to the asset file to analyze
• projectArn: ARN of the Bedrock Data Automation project to use (optional, uses default public project if not provided)

Example Usage

# List available projects
projects = await getprojects()

# Get details of a specific project
project_details = await getprojectdetails(projectArn="arn:aws:bedrock:us-east-1:123456789012:data-automation-project/my-project")

# Analyze a document
results = await analyzeasset(assetPath="/path/to/document.pdf")

# Analyze an image with a specific project
results = await analyzeasset(
    assetPath="/path/to/image.jpg",
    projectArn="arn:aws:bedrock:us-east-1:123456789012:data-automation-project/my-project"
)

Security Considerations

• Use AWS IAM roles with appropriate permissions
• Store credentials securely
• Use temporary credentials when possible
• Ensure S3 bucket permissions are properly configured

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.