$ loading_
Query S3 data lakes in natural language for discovery, analysis, and metadata exploration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "S3 Data Lake MCP Server" yet — see the docs or source repo.
Scan this S3 data lake and find CSV, JSON, or Parquet datasets related to user retention and activity. List each dataset's path, fields, and latest update time.
A list of relevant datasets with storage paths, field summaries, and update times for quick discovery.
Query order data in the S3 data lake and summarize the last 12 months by monthly revenue, order count, and average order value. Identify the strongest growth month and the sharpest decline.
A monthly trend analysis with key growth and decline periods highlighted for business review.
Inspect schema and metadata for customer behavior data in the S3 data lake. Tell me the fields, data types, partition info, and whether any key columns have high missing-value rates.
A schema, partition, and data quality overview that helps assess usability and analysis readiness.
Securely query Snowflake warehouses with natural language and retrieve data insights.
Connect AI to Snowflake for SQL, schema exploration, and data insights.
Manage SageMaker Catalog resources through natural-language Amazon DataZone API operations.
Explore Databricks metadata, run SQL, and analyze lineage for data discovery.
Securely query governed Snowflake data and use Cortex AI tools with AI agents.
Connect AI to Salesforce to inspect objects and run SOQL queries.