mcp/s3-tables-mcp-server pulls statistics

AWS S3 Tables MCP Server

Manage S3 Tables for analytics.

What is an MCP Server?

MCP Info

Attribute|Details| |-|-| Docker Image|mcp/s3-tables-mcp-server Author|awslabs Repository|https://github.com/awslabs/mcp

Image Building Info

Attribute|Details| |-|-| Dockerfile|https://github.com/awslabs/mcp/blob/636aca8e109b5559006044f035bb89c16e9e5410/src/s3-tables-mcp-server/Dockerfile Commit|636aca8e109b5559006044f035bb89c16e9e5410 Docker Image built by|Docker Inc. Docker Scout Health Score| Verify Signature|COSIGN_REPOSITORY=mcp/signatures cosign verify mcp/s3-tables-mcp-server --key https://raw.githubusercontent.com/docker/keyring/refs/heads/main/public/mcp/latest.pub Licence|Apache License 2.0

Available Tools (16)

Tools provided by this Server|Short Description -|- append_rows_to_table|Append rows to an Iceberg table using PyIceberg/Daft.| create_namespace|Create a new namespace in an S3 table bucket.| create_table|Create a new S3 table in an S3 table bucket.| create_table_bucket|Creates an S3 table bucket.| get_bucket_metadata_config|Get the metadata table configuration for a regular general purpose S3 bucket.| get_maintenance_job_status|Get the status of a maintenance job for a table.| get_table_maintenance_config|Get details about the maintenance configuration of a table.| get_table_metadata_location|Get the location of the S3 table metadata.| import_csv_to_table|Import data from a CSV file into an S3 table.| import_parquet_to_table|Import data from a Parquet file into an existing S3 table.| list_namespaces|List all namespaces across all S3 table buckets.| list_table_buckets|List all S3 table buckets for your AWS account.| list_tables|List all S3 tables across all table buckets and namespaces.| query_database|Execute SQL queries against S3 Tables using PyIceberg/Daft.| rename_table|Rename an S3 table or move it to a different S3 namespace.| update_table_metadata_location|Update the metadata location for an S3 table.|

Tools Details

Tool: append_rows_to_table

Append rows to an Iceberg table using PyIceberg/Daft.

This tool appends data rows to an existing Iceberg table using the PyIceberg engine. The rows parameter must be a list of dictionaries, each representing a row. Check the schema of the table before appending rows.

Example input values: warehouse: 'arn:aws:s3tables:::bucket/' region: 'us-west-2' namespace: 'retail_data' table_name: 'customers' rows: [{"customer_id": 1, "customer_name": "Alice"}, ...] uri: 'https://s3tables.us-west-2.amazonaws.com/iceberg' catalog_name: 's3tablescatalog' rest_signing_name: 's3tables' rest_sigv4_enabled: 'true' Parameters|Type|Description -|-|- namespace|string|The name of the namespace. Must be 1-255 characters long and contain only alphanumeric characters, underscores, and hyphens. region|string|AWS region for S3Tables/Iceberg REST endpoint rows|array|List of rows to append, each as a dict table_name|string|The name of the table. Must be 1-255 characters long and contain only alphanumeric characters, underscores, and hyphens. uri|string|REST URI for Iceberg catalog warehouse|string|Warehouse string for Iceberg catalog catalog_name|string *optional*|Catalog name rest_signing_name|string *optional*|REST signing name rest_sigv4_enabled|string *optional*|Enable SigV4 signing

Tool: create_namespace

Create a new namespace in an S3 table bucket.

Creates a namespace. A namespace is a logical grouping of tables within your S3 table bucket, which you can use to organize S3 tables.

Permissions: You must have the s3tables:CreateNamespace permission to use this operation. Parameters|Type|Description -|-|- namespace|string|The name of the namespace. Must be 1-255 characters long and contain only alphanumeric characters, underscores, and hyphens. table_bucket_arn|string|Table bucket ARN region_name|string *optional*|The AWS region name where the operation should be performed.

Tool: create_table

Create a new S3 table in an S3 table bucket.

Creates a new S3 table associated with the given S3 namespace in an S3 table bucket. The S3 table can be configured with specific format and metadata settings. Metadata contains the schema of the table. Do not use the metadata parameter if the schema is unclear.

Supported Iceberg Primitive Types: • boolean: True or false • int: 32-bit signed integers (can promote to long) • long: 64-bit signed integers • float: 32-bit IEEE 754 floating point (can promote to double) • double: 64-bit IEEE 754 floating point • decimal(P,S): Fixed-point decimal with precision P and scale S (precision must be 38 or less) • date: Calendar date without timezone or time • time: Time of day, microsecond precision, without date or timezone • timestamp: Timestamp, microsecond precision, without timezone (represents date and time regardless of zone) • timestamptz: Timestamp, microsecond precision, with timezone (stored as UTC) • string: Arbitrary-length character sequences (UTF-8 encoded)

Note: Binary field types (binary, fixed, uuid) are not supported.…

Description from the Docker Hub listing.