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ryft-io

IcebergMCP

by ryft-io

get_table_schema

Retrieve the schema for an Apache Iceberg table by specifying its namespace and table name. This tool helps users understand table structure and data types for querying and analysis in data lakehouses.

Instructions

Provides the schema for a given Iceberg table

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namespaceYes
table_nameYes

Implementation Reference

  • The handler function that loads the Iceberg table and extracts its schema fields into the defined SchemaField format.
    def get_table_schema( namespace: str, table_name: str ) -> list[SchemaField]: """Provides the schema for a given Iceberg table""" catalog: Catalog = get_catalog() table_obj = catalog.load_table((namespace, table_name)) schema = table_obj.schema() fields = [] for field in schema.fields: fields.append( { "id": field.field_id, "name": field.name, "type": str(field.field_type), "required": field.required, "doc": field.doc if field.doc else None, } ) return fields
  • Registers the get_table_schema function as an MCP tool using the FastMCP decorator.
    @mcp.tool()
  • Defines the output schema structure for table schema fields.
    class SchemaField(TypedDict): id: int name: str type: str required: bool doc: str | None
  • Helper function to initialize the GlueCatalog using AWS credentials from config.
    def get_catalog() -> GlueCatalog: try: session = boto3.Session(profile_name=iceberg_config.profile_name) credentials = session.get_credentials().get_frozen_credentials() catalog = GlueCatalog( "glue", **{ "client.access-key-id": credentials.access_key, "client.secret-access-key": credentials.secret_key, "client.session-token": credentials.token, "client.region": iceberg_config.region, }, ) except Exception as e: logger.error(f"Error creating AWS connection: {str(e)}") raise return catalog

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