export_data_slice
Export specific data slices or grids from Oracle EPM Cloud FCCS for analysis and reporting purposes.
Instructions
Export a specific data slice (grid) from the application / Exportar um slice de dados
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cube_name | No | The name of the cube (default: 'Consol') | |
| grid_definition | Yes | The data grid definition with pov, columns, and rows |
Implementation Reference
- fccs_agent/tools/data.py:21-35 (handler)The main handler function for the 'export_data_slice' tool. It takes a grid definition and optional cube name, calls the underlying FCCS client to export the data slice, and returns the result wrapped in a success status.
async def export_data_slice( grid_definition: dict[str, Any], cube_name: str = "Consol" ) -> dict[str, Any]: """Export a specific data slice (grid) from the application / Exportar um slice de dados. Args: grid_definition: The data grid definition with pov, columns, and rows. cube_name: The name of the cube (default: 'Consol'). Returns: dict: The exported data slice with rows and column values. """ result = await _client.export_data_slice(_app_name, cube_name, grid_definition) return {"status": "success", "data": result} - fccs_agent/tools/data.py:274-291 (schema)The input schema definition for the 'export_data_slice' tool, specifying the expected parameters: grid_definition (required) and optional cube_name.
{ "name": "export_data_slice", "description": "Export a specific data slice (grid) from the application / Exportar um slice de dados", "inputSchema": { "type": "object", "properties": { "cube_name": { "type": "string", "description": "The name of the cube (default: 'Consol')", }, "grid_definition": { "type": "object", "description": "The data grid definition with pov, columns, and rows", }, }, "required": ["grid_definition"], }, }, - fccs_agent/agent.py:138-185 (registration)The TOOL_HANDLERS dictionary in the agent registers 'export_data_slice' by mapping the tool name to its handler function data.export_data_slice. This is used by the execute_tool function to dispatch calls.
TOOL_HANDLERS = { # Application "get_application_info": application.get_application_info, "get_rest_api_version": application.get_rest_api_version, # Jobs "list_jobs": jobs.list_jobs, "get_job_status": jobs.get_job_status, "run_business_rule": jobs.run_business_rule, "run_data_rule": jobs.run_data_rule, # Dimensions "get_dimensions": dimensions.get_dimensions, "get_members": dimensions.get_members, "get_dimension_hierarchy": dimensions.get_dimension_hierarchy, # Journals "get_journals": journals.get_journals, "get_journal_details": journals.get_journal_details, "perform_journal_action": journals.perform_journal_action, "update_journal_period": journals.update_journal_period, "export_journals": journals.export_journals, "import_journals": journals.import_journals, # Data "export_data_slice": data.export_data_slice, "smart_retrieve": data.smart_retrieve, "smart_retrieve_consolidation_breakdown": data.smart_retrieve_consolidation_breakdown, "smart_retrieve_with_movement": data.smart_retrieve_with_movement, "copy_data": data.copy_data, "clear_data": data.clear_data, # Reports "generate_report": reports.generate_report, "get_report_job_status": reports.get_report_job_status, "generate_report_script": reports.generate_report_script, # Consolidation "export_consolidation_rulesets": consolidation.export_consolidation_rulesets, "import_consolidation_rulesets": consolidation.import_consolidation_rulesets, "validate_metadata": consolidation.validate_metadata, "generate_intercompany_matching_report": consolidation.generate_intercompany_matching_report, "import_supplementation_data": consolidation.import_supplementation_data, "deploy_form_template": consolidation.deploy_form_template, "generate_consolidation_process_report": consolidation.generate_consolidation_process_report, # Memo "generate_system_pitch": memo.generate_system_pitch, "generate_investment_memo": memo.generate_investment_memo, # Feedback "submit_feedback": feedback.submit_feedback, "get_recent_executions": feedback.get_recent_executions, # Local Data "query_local_metadata": local_data.query_local_metadata, }