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Connect to Azure Data Lake Storage Gen2 for file and metadata operations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ADLS2 MCP Server" yet — see the docs or source repo.
Using the ADLS2 MCP Server, bulk upload the CSV files from the local reports/ folder to the raw-data/2025-01/ path in Azure Data Lake Storage Gen2, keeping the original filenames. Return lists of successful and failed uploads.
A summary of upload results, including successful files, failed files, and destination paths.
Use the ADLS2 MCP Server to move all .json files under data/staging/ to data/archive/2024/ and rename them using the format source_originalfilename. Then list the updated full paths.
A list of moved and renamed files, along with their new directory paths.
Using the ADLS2 MCP Server, read the current metadata and properties of lakehouse/sales/transactions.parquet. If the owner and retention tags are missing, set them to analytics-team and 90d, then return a before-and-after comparison.
The file’s current properties, metadata update results, and a before-and-after diff summary.
Secure file and directory operations for autonomous AI development workflows.
Connect to Amazon DataZone to query and manage governed data resources.
Manage AutoDL GPU instances, SSH access, file transfers, and GPU monitoring.
Search, inspect, trace, and read business data assets across internal platforms.
Connect databases, files, AWS IAM, and Gmail through one MCP server.
Enhanced filesystem MCP tool for searching, reading, editing, deleting, and running commands.