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

Install uv from Astral or the GitHub README
Install Python using uv python install 3.10
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
Create an AWS S3 Bucket
- Example AWS CLI command to create the bucket
- ```
 aws s3 create-bucket <bucket-name>
```

Installation

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

{
  "mcpServers": {
    "awslabs.aws-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": []
    }
  }
}

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

{
  "mcpServers": {
    "awslabs.aws-bedrock-data-automation-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "--interactive",
        "--env",
        "AWS_PROFILE",
        "--env",
        "AWS_REGION",
        "--env",
        "AWS_BUCKET_NAME",
        "--env",
        "BASE_DIR",
        "--env",
        "FASTMCP_LOG_LEVEL",
        "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": []
    }
  }
}

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.

AWS Bedrock Data Automation MCP Server