get_table_data_tool
Retrieve Google Sheets table data with optional column filtering and row range selection for efficient data extraction.
Instructions
Get table data with optional column filtering using Google Sheets API.
This unified tool can retrieve all table data or specific columns based on user input.
If column_names is provided, it uses spreadsheets.values.get for efficiency.
If column_names is not provided, it uses spreadsheets.tables.get for full data.
Args:
spreadsheet_name: Name of the spreadsheet
sheet_name: Name of the sheet containing the table
table_name: Name of the table to read data from
column_names: List of column names to retrieve (optional - if not provided, gets all columns)
start_row: Starting row index (0-based, optional)
end_row: Ending row index (0-based, optional)
include_headers: Whether to include header row in results
max_rows: Maximum number of rows to return (optional)
Returns:
JSON string with table data and metadataInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| spreadsheet_name | Yes | The name of the Google Spreadsheet | |
| sheet_name | Yes | The name of the sheet containing the table | |
| table_name | Yes | Name of the table to read data from | |
| column_names | No | List of column names to retrieve (optional - if not provided, gets all columns) | |
| start_row | No | Starting row index (0-based, optional, use -1 for all rows) | |
| end_row | No | Ending row index (0-based, optional, use -1 for all rows) | |
| include_headers | No | Whether to include header row in results | |
| max_rows | No | Maximum number of rows to return (optional, use -1 for no limit) |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- Core handler function executing the tool logic: retrieves table data from Google Sheets API, handles column selection, row ranges, validation, and formats JSON response using helper utilities.
def get_table_data_handler( drive_service, sheets_service, spreadsheet_name: str, sheet_name: str, table_name: str, column_names: List[str], start_row: int, end_row: int, include_headers: bool, max_rows: int ) -> str: """ Get table data with optional column filtering using Google Sheets API. This handler can retrieve all table data or specific columns based on user input. If column_names is empty, it uses spreadsheets.tables.get for full data. If column_names is provided, it uses spreadsheets.values.get for efficiency. Args: drive_service: Google Drive service instance sheets_service: Google Sheets service instance spreadsheet_name: Name of the spreadsheet sheet_name: Name of the sheet containing the table table_name: Name of the table to read data from column_names: List of column names to retrieve (empty list for all columns) start_row: Starting row index (0-based, -1 for all rows) end_row: Ending row index (0-based, -1 for all rows) include_headers: Whether to include header row in results max_rows: Maximum number of rows to return (-1 for no limit) Returns: str: Success message with table data or error message """ try: # Validate inputs if not table_name or table_name.strip() == "": return compact_json_response({ "success": False, "message": "Table name is required." }) # Convert -1 values to None for optional parameters if start_row == -1: start_row = None if end_row == -1: end_row = None if max_rows == -1: max_rows = None # Validate row indices if start_row is not None and start_row < 0: return compact_json_response({ "success": False, "message": "start_row must be non-negative." }) if end_row is not None and end_row < 0: return compact_json_response({ "success": False, "message": "end_row must be non-negative." }) if start_row is not None and end_row is not None and start_row >= end_row: return compact_json_response({ "success": False, "message": "start_row must be less than end_row." }) if max_rows is not None and max_rows <= 0: return compact_json_response({ "success": False, "message": "max_rows must be positive." }) # Get spreadsheet ID spreadsheet_id = get_spreadsheet_id_by_name(drive_service, spreadsheet_name) if not spreadsheet_id: return compact_json_response({ "success": False, "message": f"Spreadsheet '{spreadsheet_name}' not found." }) # Get sheet ID sheet_ids = get_sheet_ids_by_names(sheets_service, spreadsheet_id, [sheet_name]) sheet_id = sheet_ids.get(sheet_name) if sheet_id is None: return compact_json_response({ "success": False, "message": f"Sheet '{sheet_name}' not found in spreadsheet '{spreadsheet_name}'." }) # Get table ID table_ids = get_table_ids_by_names(sheets_service, spreadsheet_id, sheet_name, [table_name]) table_id = table_ids.get(table_name) if not table_id: return compact_json_response({ "success": False, "message": f"Table '{table_name}' not found in sheet '{sheet_name}'." }) # Get table information try: table_info = get_table_info(sheets_service, spreadsheet_id, table_id) table_range = table_info.get('range', {}) columns = table_info.get('columns', []) except Exception as e: return compact_json_response({ "success": False, "message": f"Could not retrieve table information: {str(e)}" }) # Extract table range information start_row_index = table_range.get('startRowIndex', 0) end_row_index = table_range.get('endRowIndex', 0) start_column_index = table_range.get('startColumnIndex', 0) end_column_index = table_range.get('endColumnIndex', 0) # Determine if we're getting specific columns or all columns is_specific_columns = len(column_names) > 0 if is_specific_columns: # Validate column names column_name_to_index = {col.get('name', ''): col.get('index', 0) for col in columns} target_column_indices = [] for col_name in column_names: if col_name not in column_name_to_index: return compact_json_response({ "success": False, "message": f"Column '{col_name}' not found in table." }) target_column_indices.append(column_name_to_index[col_name]) # Sort column indices to maintain order target_column_indices.sort() # Convert column indices to letters for API call column_letters = [] for col_index in target_column_indices: absolute_col_index = start_column_index + col_index column_letter = column_index_to_letter(absolute_col_index) column_letters.append(column_letter) # Create range string for API call if len(column_letters) == 1: range_string = f"{sheet_name}!{column_letters[0]}:{column_letters[0]}" else: range_string = f"{sheet_name}!{column_letters[0]}:{column_letters[-1]}" # Adjust range for row limits if specified if start_row is not None or end_row is not None: actual_start_row = start_row if start_row is not None else start_row_index actual_end_row = end_row if end_row is not None else end_row_index # Convert to 1-based row numbers for API start_row_num = actual_start_row + 1 end_row_num = actual_end_row range_string = f"{sheet_name}!{column_letters[0]}{start_row_num}:{column_letters[-1]}{end_row_num}" # Get column data using spreadsheets.values.get try: values_response = sheets_service.spreadsheets().values().get( spreadsheetId=spreadsheet_id, range=range_string ).execute() values = values_response.get('values', []) except Exception as e: return compact_json_response({ "success": False, "message": f"Could not retrieve column data: {str(e)}" }) # Process specific columns data processed_rows = [] target_column_names = [] for col_index in target_column_indices: if col_index < len(columns): col_name = columns[col_index].get('name', f'Column {col_index}') target_column_names.append(col_name) else: target_column_names.append(f'Column {col_index}') # Process each row for row_index, row in enumerate(values): # Skip header row if not included if not include_headers and row_index == 0: continue # Create row data with column mapping row_data = {} for i, col_name in enumerate(target_column_names): if i < len(row): row_data[col_name] = row[i] else: row_data[col_name] = None processed_rows.append({ "row_index": row_index, "data": row_data }) # Apply max_rows limit if specified if max_rows is not None and len(processed_rows) > max_rows: processed_rows = processed_rows[:max_rows] response_data = { "success": True, "spreadsheet_name": spreadsheet_name, "sheet_name": sheet_name, "table_name": table_name, "columns_requested": target_column_names, "column_indices": target_column_indices, "range_used": range_string, "total_rows": len(processed_rows), "rows": processed_rows, "message": f"Successfully retrieved data for {len(target_column_names)} column(s) from table '{table_name}'" } else: # Get all table data using spreadsheets.values.get with table range try: # Construct range string for the entire table range_string = f"{sheet_name}!A{start_row_index + 1}:{column_index_to_letter(end_column_index - 1)}{end_row_index}" values_response = sheets_service.spreadsheets().values().get( spreadsheetId=spreadsheet_id, range=range_string ).execute() values = values_response.get('values', []) except Exception as e: return compact_json_response({ "success": False, "message": f"Could not retrieve table data: {str(e)}" }) # Get column names column_names_all = [col.get('name', f'Column {i}') for i, col in enumerate(columns)] # Process rows based on parameters processed_rows = [] total_rows = len(values) # Determine row range actual_start_row = start_row if start_row is not None else 0 actual_end_row = end_row if end_row is not None else total_rows # Validate row range if actual_start_row >= total_rows: return compact_json_response({ "success": False, "message": f"start_row ({actual_start_row}) is beyond table size ({total_rows})." }) if actual_end_row > total_rows: actual_end_row = total_rows # Extract rows within range rows_in_range = values[actual_start_row:actual_end_row] # Apply max_rows limit if specified if max_rows is not None and len(rows_in_range) > max_rows: rows_in_range = rows_in_range[:max_rows] # Process each row for i, row in enumerate(rows_in_range): row_index = actual_start_row + i # Create row object processed_row = { "row_index": row_index, "data": row } # Add column mapping if headers are included if include_headers and len(column_names_all) == len(row): processed_row["column_data"] = dict(zip(column_names_all, row)) processed_rows.append(processed_row) response_data = { "success": True, "spreadsheet_name": spreadsheet_name, "sheet_name": sheet_name, "table_name": table_name, "table_info": { "total_rows": total_rows, "total_columns": len(column_names_all), "column_names": column_names_all, "start_row": actual_start_row, "end_row": actual_end_row, "rows_returned": len(processed_rows) }, "rows": processed_rows, "message": f"Successfully retrieved all data from table '{table_name}'" } return compact_json_response(response_data) except HttpError as error: return compact_json_response({ "success": False, "message": f"Google Sheets API error: {str(error)}" }) except Exception as e: return compact_json_response({ "success": False, "message": f"Error getting table data: {str(e)}" }) - gsheet_mcp_server/server.py:596-629 (registration)Registers the MCP tool 'get_table_data_tool' with @mcp.tool() decorator, defines input schema using Pydantic Field descriptions and defaults, and serves as a thin wrapper calling the core handler function.
@mcp.tool() def get_table_data_tool( spreadsheet_name: str = Field(..., description="The name of the Google Spreadsheet"), sheet_name: str = Field(..., description="The name of the sheet containing the table"), table_name: str = Field(..., description="Name of the table to read data from"), column_names: List[str] = Field(default=[], description="List of column names to retrieve (optional - if not provided, gets all columns)"), start_row: int = Field(default=-1, description="Starting row index (0-based, optional, use -1 for all rows)"), end_row: int = Field(default=-1, description="Ending row index (0-based, optional, use -1 for all rows)"), include_headers: bool = Field(default=True, description="Whether to include header row in results"), max_rows: int = Field(default=-1, description="Maximum number of rows to return (optional, use -1 for no limit)") ) -> str: """ Get table data with optional column filtering using Google Sheets API. This unified tool can retrieve all table data or specific columns based on user input. If column_names is provided, it uses spreadsheets.values.get for efficiency. If column_names is not provided, it uses spreadsheets.tables.get for full data. Args: spreadsheet_name: Name of the spreadsheet sheet_name: Name of the sheet containing the table table_name: Name of the table to read data from column_names: List of column names to retrieve (optional - if not provided, gets all columns) start_row: Starting row index (0-based, optional) end_row: Ending row index (0-based, optional) include_headers: Whether to include header row in results max_rows: Maximum number of rows to return (optional) Returns: JSON string with table data and metadata """ sheets_service, drive_service = _get_google_services() return get_table_data_handler(drive_service, sheets_service, spreadsheet_name, sheet_name, table_name, column_names, start_row, end_row, include_headers, max_rows)