get_chart_of_accounts
ChatGPTGet the Chart of Accounts for this company
Data-Analytics
Overview
Cube is the agentic analytics platform, built on a semantic layer. This connector brings it to where you already work. The semantic layer is what makes the AI useful. Your data team defines metrics, dimensions and joins once, and Claude answers against those definitions rather than raw tables — "revenue" means what your team decided it means, not what a column name implies. Claude can search the model, run read-only SQL through the Cube SQL API, and page through large results. It can also build and publish dashboards, edit existing reports, and — for users with data-model permissions — read and edit the semantic model's source files on a dev branch that never touches production. Access follows your existing Cube permissions: users only see the deployments, views and model files their role grants them, and row-level security applies to every query. Requires a Cube account on a Premium or Enterprise plan, with the Viewer role or higher.
Tools
get_drilldown_data
ChatGPTUse this for TRANSACTION-LEVEL auditing. This is the lowest level of detail available. Use this when a user asks for "source data," "specific transactions," or "the details behind this number." A header will be present for each dimension the company has, the value of the data point, as well as any additional metadata that is stored about that data point. IMPORTANT: The IDs for 'filter_dimension_ids' MUST be retrieved first using the 'get_chart_of_accounts' tool. Do not guess these IDs. All dimensions must be represented in this dictionary. Prefer using 'get_financial_slice_of_data' for aggregated reporting. Only use this tool when transaction-level detail is specifically requested. Args: filter_dimension_ids: A dictionary where each key is a dimension name and each value is the ID of a single child from that dimension in the Chart of Accounts. All dimensions must be represented in this dictionary. operation: A pandas DataFrame operation to run on the drilldown data. Common operations: - 'head': Returns first N rows (e.g., {'n': 10}). Use to preview the data structure. - 'sort_values': Find outliers (e.g., {'by': 'Value', 'ascending': False}). - 'query': Filter further (e.g., {'expr': 'Value > 10000'}). - 'sum': Aggregate a column (e.g., {'numeric_only': True}). operation_parameters: The parameters to pass to the pandas operation. See pandas DataFrame documentation for operation-specific parameters. ctx: The context of the request. This is used to get the user company from the request context. Returns: The result of the pandas operation as a string. If the resulting DataFrame exceeds 500 rows, it will be automatically truncated with a warning. If you see a truncation warning, refine your 'operation_parameters' to be more specific (e.g., use more filters or aggregation operations like 'sum()', 'mean()', etc.).
get_financial_slice_of_data
ChatGPTUse this function to get a slice of financial data for this company. This function acts like a pivot table in a spreadsheet and will show a slice of financial data. The row and column headers will be on the top and left side of the grid. The filters will be applied to the data in the grid. Think of this as building a Summary Table. It aggregates data into a grid based on the dimensions you provide. IMPORTANT: The IDs for 'filters', 'row_header_dimension_ids', and 'column_header_dimension_ids' MUST be retrieved first using the 'get_chart_of_accounts' tool. Do not guess these IDs. Args: filters: A dictionary where each key is a dimension name and each value is the ID of a single child from that dimension in the Chart of Accounts. If a dimension is included in the filters, it should not be included in the row or column headers. These are global constraints on the data. row_header_dimension_ids: A list of IDs of children from a single dimension. This dimension IDs must be from the company's Chart of Accounts. This cannot be empty. column_header_dimension_ids: A list of IDs of children from a single dimension. This dimension IDs must be from the company's Chart of Accounts. This cannot be empty. ctx: The context of the request. This is used to get the user company from the request context. Returns: A slice of financial data comprised of a grid of values with their corresponding row and column header dimension names added in the first row and column.
list_companies
ChatGPTList all companies the user has access to
select_company
ChatGPTSelects which company of the ones the user has access to is used for requests. Args: company_id: The ID of the company to select. ctx: The context of the request. This is used to get information about the user's session. Returns: A message indicating the company has been selected.
buildPreAggregation
Claudechat
ClaudecreateReport
ClaudecreateWorkbook
ClaudedeleteDataModelFile
ClaudedeleteReport
ClaudegetBranchDiff
ClaudegetDataModelChanges
ClaudegetDeploymentEnv
ClaudegetPreAggregationStatus
ClaudelistDataModelFiles
ClaudelistDeployments
ClaudeloadQueryResults
ClaudepublishDashboard
ClaudereadDataModelFile
ClaudereadReport
ClaudereadWorkbook
ClauderunQuery
ClaudesearchDataModel
ClaudestartDataModelEdit
ClaudeupdateDashboard
ClaudeupdateReport
ClaudewriteDataModelFile
ClaudeLinks
App Stats
28
Tools
1
Prompts
ChatGPT, Claude
Platforms
Category
Data ExplorationWorks with
ChatGPT
Claude
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