AWS Glue DataBrew MCP Server
The AWS Glue DataBrew API, provided by Amazon Web Services, serves as the programmatic backbone for DataBrew, a fully managed, visual data preparation service designed to accelerate data processing for analytics and machine learning.
Quick Start Summary
The AWS Glue DataBrew MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the AWS Glue DataBrew API through natural language. It exposes 10 API endpoints as callable tools, such as BatchDeleteRecipeVersion, ListDatasets, CreateDataset, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-databrew. This integration is sourced from the auto AWS Glue DataBrew OpenAPI specification (v2017-07-25) and has a quality score of 46/99 (fair documentation coverage).
Server Details
- Category
- Cloud Infrastructure
- Authentication
- None
- Endpoints
- 10 operations
- Transport
- STDIO
- Spec Version
- v2017-07-25
- Install Command
npx -y @mcp/amazonaws-com-databrew
Environment Variables
AWS_GLUE_DATABREW_API_KEYExample: your_aws_glue_databrew_api_key
Top Endpoints
/recipes/{name}/batchDeleteRecipeVersionBatchDeleteRecipeVersion
/datasetsListDatasets
/datasetsCreateDataset
/profileJobsCreateProfileJob
/projectsListProjects
One-Click Install
Copy the snippet for your MCP client and paste it in — zero editing required.
Claude Desktop
Add to claude_desktop_config.json
{
"mcpServers": {
"amazonaws-com-databrew": {
"command": "npx",
"args": [
"-y",
"@mcp/amazonaws-com-databrew"
],
"env": {
"AWS_GLUE_DATABREW_API_KEY": "your_aws_glue_databrew_api_key"
}
}
}
}Cursor
Settings → MCP Servers → Add
{
"mcpServers": {
"amazonaws-com-databrew": {
"url": "https://mcpbridge.org/config/amazonaws-com-databrew.json"
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code
Use with MCP extension
{
"mcpServers": {
"amazonaws-com-databrew": {
"url": "https://mcpbridge.org/config/amazonaws-com-databrew.json"
}
}
}Endpoints Explorer
Search and browse the 10 operations supported by this server.
Multi-Language Code Examples
Executable code snippets for calling AWS Glue DataBrew endpoints in curl, TypeScript, or Python.
curl -X POST "https://api.apis.guru/v2/specs/amazonaws.com/databrew/2017-07-25/recipes/{name}/batchDeleteRecipeVersion" \
-H "Content-Type: application/json" \
# No auth requiredManual Configuration
Directly add this block to your JSON config file, or use the hosted config registry URL.
{
"mcpServers": {
"amazonaws-com-databrew": {
"command": "npx",
"args": ["-y","@mcp/amazonaws-com-databrew"],
"env": {
"AWS_GLUE_DATABREW_API_KEY": "your_aws_glue_databrew_api_key"
}
}
}
}Authentication Details
No authentication required. This MCP server runs out-of-the-box.
Documentation Links
Error Handling & HTTP Status Code Matrix
Common status codes, error causes, and resolution steps when invoking AWS Glue DataBrew endpoints.
400 Bad RequestCause: Malformed payload parameters or missing required fields.
Resolution: Verify request schema in Endpoints tab before calling tool.
401 UnauthorizedCause: Missing or invalid API key credentials.
Resolution: Set environment variable in MCP client config under env object.
403 ForbiddenCause: Insufficient scope permissions for requested resource.
Resolution: Verify key permissions in developer dashboard.
404 Not FoundCause: Resource URL path or requested entity ID does not exist.
Resolution: Check path variables and parameters.
429 Rate Limit ExceededCause: API rate limit quota exceeded.
Resolution: Implement exponential backoff retry in tool call.
500 Internal ErrorCause: Upstream server runtime fault.
Resolution: Inspect STDIO stderr output for log trace.
Quality Score
Checked against our protocol-compliance rules.
Specification Version: v2017-07-25
Page compiled on: June 13, 2026
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📖 Detailed MCP Integration Guide
A technical breakdown of capabilities, agent workflows, and security/configuration best practices.
databrew:ListDatasets and databrew:GetDataset for read-only monitoring, or specific job and recipe action permissions for operational tasks, rather than broad administrative rights. Developers must ensure these credentials are never exposed and are managed via secure environment variables or a secrets manager. Additionally, enabling AWS CloudTrail logging for DataBrew API calls provides a vital audit trail for all actions performed by the AI agent through the MCP server.Similar APIs
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