AWS Bedrock Data Automation MCP Server

What is AWS Bedrock Data Automation MCP Server?

AWS Bedrock Data Automation MCP Server is a specialized MCP server that bridges language model agents and Amazon Bedrock’s foundation model-powered Data Automation features. It allows LLMs (such as Claude, Amazon Q Developer, Cursor, or Cline) to securely invoke complex data processing operations—such as extracting structured information from documents, analyzing the content of images, or transcribing speech from audio files. By making these operations available as standard MCP Tools, this server streamlines the automation of rich data tasks directly within development workflows or chatbot interfaces.

How to Configure AWS Bedrock Data Automation MCP Server

1. Install Prerequisites: Ensure you have uv and Python 3.10+ installed. Set up AWS credentials with access to required Bedrock and data automation services.
2. Configure MCP Client: Add configuration for the MCP server in your tool (e.g., Amazon Q CLI, Cline, Cursor, Windsurf). For example, add the following entry to your MCP config:

   {
     "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",
         "FASTMCP_LOG_LEVEL": "ERROR"
       }
     }
   }
   

3. Environment Variables: Set AWS_PROFILE and AWS_REGION according to your AWS setup, and provide any additional required environment variables for access control or file storage.
4. (Optional) Container Deployment: You may choose to run the server in a Docker container, setting environment variables with --env and mounting necessary volumes for file access.

How to Use AWS Bedrock Data Automation MCP Server

1. Discover Available Tools: From your agentic client or chat interface, list available tools to see what Bedrock Data Automation functions are exposed.
2. Invoke Tools: Prompt your LLM assistant with natural language, specifying the type of data automation task (e.g., "Extract tables from this PDF" or "Transcribe this audio file using Bedrock Data Automation").
3. File Input: Depending on client integration, provide file paths or upload files as part of your request.
4. Results: The MCP server processes the files via Bedrock APIs and returns structured, actionable results to your assistant for further workflow steps or direct user presentation.
5. Automation in Workflows: Use these tools as building blocks to automate multi-modal, multi-step document and media workflows entirely from natural language instructions.

Key Features

• Real-time, LLM-powered access to Amazon Bedrock Data Automation for documents, images, audio, and video.
• Unified interface that abstracts document parsing, OCR, text recognition, table extraction, entity detection, and transcription.
• Secure, flexible deployment within your AWS environment: no data leaves your infrastructure unless permitted.
• Integrates with code assistants (Amazon Q Developer, Cursor, Cline), chat agents, and headless workflow automation tools.
• Follows Model Context Protocol standards for tool listing, invocation, and result formatting—interoperable with other MCP ecosystem servers.
• Supports both interactive (user-in-the-loop) and autonomous (background) use cases.

Use Cases

• Extract tabular or structured data from invoices, contracts, or forms as part of document ingestion flows.
• Analyze and summarize images or graphics submitted by users via chat, including text detection and scene description.
• Transcribe and enrich audio or video files uploaded by team members for meeting notes or archival.
• Automate compliance checks by extracting and validating key features from uploaded documents.
• Integrate document and media processing into developer IDE copilots or business workflow bots, reducing the need for custom scripting.

FAQ

Q1: What file types are supported by AWS Bedrock Data Automation MCP Server?

A1: It supports a wide array of document (PDF, DOCX, image formats like PNG/JPEG), audio (MP3, WAV), and video files, depending on the specific Bedrock Agent capabilities enabled in your account.

Q2: Do I need to upload files to S3 to use the server?

A2: Depending on your agent's integration, files may be uploaded directly, referenced by path, or programmatically moved to S3 as part of the workflow. The server and client handle these logistics transparently in most cases.

Q3: Is my data sent outside my AWS account or region?

A3: No, all operations are processed securely within your specified AWS region and do not leave your environment unless explicitly configured otherwise.

Q4: Can I combine this server with other MCP servers in a single workflow?

A4: Yes! The standardized MCP interface allows you to chain Bedrock Data Automation tasks with other servers (e.g., loading extracted data into Redshift via the Redshift MCP Server).

Q5: What permissions are required to use AWS Bedrock Data Automation MCP Server?

A5: You need Bedrock agent invocation permissions, plus read and write access to S3 if file storage is involved. Further permissions may be required for downstream workflow integration.