list_tables
Retrieve detailed information on ClickHouse tables in a specified database, including schema, comment, row count, and column count. Filter results using 'like' and 'not like' patterns for precise table listing.
Instructions
List available ClickHouse tables in a database, including schema, comment, row count, and column count.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | ||
| like | No | ||
| not_like | No |
Implementation Reference
- mcp_clickhouse/mcp_server.py:100-132 (handler)The @mcp.tool()-decorated handler function that implements the list_tables tool. It queries system.tables for table metadata in the given database (optionally filtered by like/not_like), then for each table queries system.columns to fetch column details, constructs Table and Column dataclasses, and returns serialized dicts.
@mcp.tool() def list_tables( database: str, like: Optional[str] = None, not_like: Optional[str] = None ): """List available ClickHouse tables in a database, including schema, comment, row count, and column count.""" logger.info(f"Listing tables in database '{database}'") client = create_clickhouse_client() query = f"SELECT database, name, engine, create_table_query, dependencies_database, dependencies_table, engine_full, sorting_key, primary_key, total_rows, total_bytes, total_bytes_uncompressed, parts, active_parts, total_marks, comment FROM system.tables WHERE database = {format_query_value(database)}" if like: query += f" AND name LIKE {format_query_value(like)}" if not_like: query += f" AND name NOT LIKE {format_query_value(not_like)}" result = client.query(query) # Deserialize result as Table dataclass instances tables = result_to_table(result.column_names, result.result_rows) for table in tables: column_data_query = f"SELECT database, table, name, type AS column_type, default_kind, default_expression, comment FROM system.columns WHERE database = {format_query_value(database)} AND table = {format_query_value(table.name)}" column_data_query_result = client.query(column_data_query) table.columns = [ c for c in result_to_column( column_data_query_result.column_names, column_data_query_result.result_rows, ) ] logger.info(f"Found {len(tables)} tables") return [asdict(table) for table in tables] - mcp_clickhouse/mcp_server.py:27-45 (schema)Table dataclass defining the output schema for tables returned by list_tables.
@dataclass class Table: database: str name: str engine: str create_table_query: str dependencies_database: str dependencies_table: str engine_full: str sorting_key: str primary_key: str total_rows: int total_bytes: int total_bytes_uncompressed: int parts: int active_parts: int total_marks: int comment: Optional[str] = None columns: List[Column] = field(default_factory=list) - mcp_clickhouse/mcp_server.py:16-25 (schema)Column dataclass defining the schema for table columns.
@dataclass class Column: database: str table: str name: str column_type: str default_kind: Optional[str] default_expression: Optional[str] comment: Optional[str] - mcp_clickhouse/mcp_server.py:72-73 (helper)Helper function to convert query results to List[Table].
def result_to_table(query_columns, result) -> List[Table]: return [Table(**dict(zip(query_columns, row))) for row in result] - mcp_clickhouse/mcp_server.py:76-77 (helper)Helper function to convert query results to List[Column].
def result_to_column(query_columns, result) -> List[Column]: return [Column(**dict(zip(query_columns, row))) for row in result]