data_profile
Analyze Excel or CSV files to generate comprehensive data profiling reports that identify column statistics, patterns, and quality issues for data validation and cleaning.
Instructions
Generate comprehensive data profiling report for all columns
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filePath | Yes | Path to the CSV or Excel file | |
| sheet | No | Sheet name for Excel files (optional) |
Implementation Reference
- src/handlers/analytics.ts:167-235 (handler)The core handler function that executes the data_profile tool. It reads the file, analyzes each column for data type, completeness, uniqueness, and basic statistics, then returns a comprehensive JSON profile.async dataProfile(args: ToolArgs): Promise<ToolResponse> { const { filePath, sheet } = args; const data = await readFileContent(filePath, sheet); if (data.length === 0) { throw new Error('File is empty'); } const headers = data[0]; const profile: Record<string, any> = { overview: { totalRows: data.length - 1, totalColumns: headers.length, fileName: filePath.split('/').pop() || filePath }, columns: {} }; // Analyze each column for (let colIdx = 0; colIdx < headers.length; colIdx++) { const columnName = headers[colIdx]; const values = data.slice(1).map(row => row[colIdx]); const nonEmptyValues = values.filter(val => val !== '' && val !== null && val !== undefined); // Detect data type const numericValues = nonEmptyValues.map(Number).filter(val => !isNaN(val)); const isNumeric = numericValues.length > nonEmptyValues.length * 0.8; const columnProfile: Record<string, any> = { dataType: isNumeric ? 'Numeric' : 'Text', totalValues: values.length, nonEmptyValues: nonEmptyValues.length, emptyValues: values.length - nonEmptyValues.length, uniqueValues: new Set(nonEmptyValues).size, duplicateValues: nonEmptyValues.length - new Set(nonEmptyValues).size }; if (isNumeric && numericValues.length > 0) { const mean = numericValues.reduce((a, b) => a + b, 0) / numericValues.length; columnProfile.statistics = { min: Math.min(...numericValues), max: Math.max(...numericValues), mean: Math.round(mean * 100) / 100, median: numericValues.sort((a, b) => a - b)[Math.floor(numericValues.length / 2)] }; } else { // Text analysis const lengths = nonEmptyValues.map(val => String(val).length); if (lengths.length > 0) { columnProfile.textAnalysis = { minLength: Math.min(...lengths), maxLength: Math.max(...lengths), avgLength: Math.round(lengths.reduce((a, b) => a + b, 0) / lengths.length * 100) / 100 }; } } profile.columns[columnName] = columnProfile; } return { content: [ { type: 'text', text: JSON.stringify({ success: true, profile }, null, 2), }, ], }; }
- src/index.ts:345-361 (schema)The input schema definition for the data_profile tool, specifying parameters like filePath and optional sheet.name: 'data_profile', description: 'Generate comprehensive data profiling report for all columns', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath'], }, },
- src/index.ts:1225-1226 (registration)Registration of the data_profile tool in the CallToolRequestSchema handler, dispatching calls to the AnalyticsHandler.dataProfile method.case 'data_profile': return await this.analyticsHandler.dataProfile(toolArgs);
- src/index.ts:68-1190 (registration)The tool is listed in the ListTools response, making it discoverable by MCP clients. This includes the schema.this.server.setRequestHandler(ListToolsRequestSchema, async () => ({ tools: [ // Basic Data Tools { name: 'read_file', description: 'Read an entire CSV or Excel file with optional chunking for large files', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional, defaults to first sheet)', }, offset: { type: 'number', description: 'Starting row index for chunked reading (0-based, optional)', }, limit: { type: 'number', description: 'Maximum number of rows to return (optional, enables chunking)', }, }, required: ['filePath'], }, }, { name: 'get_cell', description: 'Get the value of a specific cell using A1 notation', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, cell: { type: 'string', description: 'Cell address in A1 notation (e.g., "A1", "B5")', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'cell'], }, }, { name: 'get_range', description: 'Get values from a range of cells', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, startCell: { type: 'string', description: 'Start cell in A1 notation (e.g., "A1")', }, endCell: { type: 'string', description: 'End cell in A1 notation (e.g., "D10")', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'startCell', 'endCell'], }, }, { name: 'get_headers', description: 'Get the column headers (first row) of a file', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath'], }, }, { name: 'search', description: 'Search for cells containing a specific value', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, searchValue: { type: 'string', description: 'Value to search for', }, exact: { type: 'boolean', description: 'Whether to match exactly or contains (default: false)', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'searchValue'], }, }, { name: 'filter_rows', description: 'Filter rows based on column values', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, column: { type: 'string', description: 'Column name or index (0-based)', }, condition: { type: 'string', description: 'Condition: equals, contains, greater_than, less_than', enum: ['equals', 'contains', 'greater_than', 'less_than'], }, value: { type: 'string', description: 'Value to compare against', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'column', 'condition', 'value'], }, }, { name: 'aggregate', description: 'Perform aggregation operations on a column', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, column: { type: 'string', description: 'Column name or index (0-based)', }, operation: { type: 'string', description: 'Aggregation operation', enum: ['sum', 'average', 'count', 'min', 'max'], }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'column', 'operation'], }, }, // Chunked Reading Tools { name: 'read_file_chunked', description: 'Read large files in manageable chunks to avoid token limits', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, chunkIndex: { type: 'number', description: 'Chunk index to read (0-based, defaults to 0)', default: 0, }, chunkSize: { type: 'number', description: 'Number of rows per chunk (optional, auto-calculated if not provided)', }, }, required: ['filePath'], }, }, { name: 'get_file_info', description: 'Analyze file size and get chunking recommendations', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath'], }, }, // Analytics Tools { name: 'statistical_analysis', description: 'Perform comprehensive statistical analysis on a column', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, column: { type: 'string', description: 'Column name or index (0-based)', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'column'], }, }, { name: 'correlation_analysis', description: 'Calculate correlation between two numeric columns', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, column1: { type: 'string', description: 'First column name or index (0-based)', }, column2: { type: 'string', description: 'Second column name or index (0-based)', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'column1', 'column2'], }, }, { name: 'data_profile', description: 'Generate comprehensive data profiling report for all columns', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath'], }, }, { name: 'pivot_table', description: 'Create pivot table with grouping and aggregation', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file', }, groupBy: { type: 'string', description: 'Column to group by', }, aggregateColumn: { type: 'string', description: 'Column to aggregate', }, operation: { type: 'string', description: 'Aggregation operation', enum: ['sum', 'average', 'count', 'min', 'max'], }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, }, required: ['filePath', 'groupBy', 'aggregateColumn', 'operation'], }, }, // Financial Analysis Tools (CFO-Level) { name: 'dcf_analysis', description: 'Perform Discounted Cash Flow (DCF) valuation analysis for investment evaluation', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file with cash flow data' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' }, assumptions: { type: 'object', description: 'DCF assumptions (optional)', properties: { initialInvestment: { type: 'number', description: 'Initial investment amount (negative)' }, growthRate: { type: 'number', description: 'Annual growth rate (0.15 = 15%)' }, discountRate: { type: 'number', description: 'Discount rate/WACC (0.12 = 12%)' }, terminalMultiple: { type: 'number', description: 'Terminal value multiple (8x)' }, projectionYears: { type: 'number', description: 'Number of projection years' } } } }, required: ['filePath'] } }, { name: 'budget_variance_analysis', description: 'Analyze budget vs actual performance with variance calculations', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file with budget and actual data' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' }, actualColumn: { type: 'string', description: 'Column name or index containing actual values' }, budgetColumn: { type: 'string', description: 'Column name or index containing budget values' } }, required: ['filePath', 'actualColumn', 'budgetColumn'] } }, { name: 'ratio_analysis', description: 'Perform comprehensive financial ratio analysis with industry benchmarks', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file with financial statement data' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' } }, required: ['filePath'] } }, { name: 'scenario_modeling', description: 'Perform what-if scenario analysis with multiple assumptions', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file with base data' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' }, scenarios: { type: 'array', description: 'Array of scenario definitions', items: { type: 'object', properties: { name: { type: 'string', description: 'Scenario name' }, assumptions: { type: 'object', description: 'Key-value pairs of assumption changes', additionalProperties: { type: 'number' } } }, required: ['name', 'assumptions'] } } }, required: ['filePath', 'scenarios'] } }, { name: 'trend_analysis', description: 'Analyze time series trends, growth rates, seasonality, and forecasting for sales and performance data', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file with time series data' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' }, dateColumn: { type: 'string', description: 'Column name or index containing date/time values' }, valueColumn: { type: 'string', description: 'Column name or index containing numeric values to analyze' }, periods: { type: 'number', description: 'Number of future periods to forecast (default: 12)', default: 12 } }, required: ['filePath', 'dateColumn', 'valueColumn'] } }, // File Operations Tools { name: 'write_file', description: 'Write data to a new CSV or Excel file (supports multiple sheets for Excel)', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path for the new file (must end with .csv, .xlsx, or .xls)', }, data: { type: 'array', description: 'Array of arrays representing rows of data (single sheet mode)', items: { type: 'array', }, }, headers: { type: 'array', description: 'Optional headers for the first row (single sheet mode)', items: { type: 'string', }, }, sheet: { type: 'string', description: 'Sheet name for Excel files (single sheet mode, defaults to "Sheet1")', }, sheets: { type: 'array', description: 'Array of sheet objects for multi-sheet Excel files', items: { type: 'object', properties: { name: { type: 'string', description: 'Sheet name', }, data: { type: 'array', description: 'Array of arrays representing rows of data', items: { type: 'array', }, }, headers: { type: 'array', description: 'Optional headers for the first row', items: { type: 'string', }, }, }, required: ['name', 'data'], }, }, }, required: ['filePath'], }, }, { name: 'add_sheet', description: 'Add a new sheet to an existing Excel file', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the existing Excel file (.xlsx or .xls)', }, sheetName: { type: 'string', description: 'Name for the new sheet', }, data: { type: 'array', description: 'Array of arrays representing rows of data', items: { type: 'array', }, }, headers: { type: 'array', description: 'Optional headers for the first row', items: { type: 'string', }, }, position: { type: 'number', description: 'Position to insert the sheet (0-based index, optional)', }, }, required: ['filePath', 'sheetName', 'data'], }, }, { name: 'write_multi_sheet', description: 'Create a complex Excel file with multiple sheets, formulas, and inter-sheet references', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path for the new Excel file (must end with .xlsx or .xls)', }, sheets: { type: 'array', description: 'Array of sheet definitions', items: { type: 'object', properties: { name: { type: 'string', description: 'Sheet name', }, data: { type: 'array', description: 'Array of arrays representing rows of data', items: { type: 'array', }, }, headers: { type: 'array', description: 'Optional headers for the first row', items: { type: 'string', }, }, formulas: { type: 'array', description: 'Array of formula definitions', items: { type: 'object', properties: { cell: { type: 'string', description: 'Cell address in A1 notation (e.g., "A1", "B5")', }, formula: { type: 'string', description: 'Excel formula (e.g., "=SUM(A1:A10)", "=Sheet1!A1+Sheet2!B2")', }, }, required: ['cell', 'formula'], }, }, }, required: ['name', 'data'], }, }, sheetReferences: { type: 'boolean', description: 'Enable inter-sheet formula references (default: true)', }, }, required: ['filePath', 'sheets'], }, }, { name: 'export_analysis', description: 'Export analysis results (pivot tables, statistics, etc.) to a new file', inputSchema: { type: 'object', properties: { analysisType: { type: 'string', description: 'Type of analysis to export', enum: ['pivot_table', 'statistical_analysis', 'correlation', 'data_profile'], }, sourceFile: { type: 'string', description: 'Path to the source data file', }, outputFile: { type: 'string', description: 'Path for the output file', }, analysisParams: { type: 'object', description: 'Parameters for the analysis (depends on analysisType)', }, }, required: ['analysisType', 'sourceFile', 'outputFile', 'analysisParams'], }, }, { name: 'format_cells', description: 'Apply formatting to Excel cells (fonts, colors, borders, alignment)', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the Excel file (.xlsx or .xls)', }, range: { type: 'string', description: 'Cell range in A1 notation (e.g., "A1", "A1:C5", "B2:D10")', }, styling: { type: 'object', description: 'Formatting options to apply', properties: { font: { type: 'object', description: 'Font styling options', properties: { bold: { type: 'boolean', description: 'Make text bold' }, italic: { type: 'boolean', description: 'Make text italic' }, underline: { type: 'boolean', description: 'Underline text' }, size: { type: 'number', description: 'Font size (e.g., 12, 14, 16)' }, color: { type: 'string', description: 'Font color in ARGB format (e.g., "FFFF0000" for red)' }, name: { type: 'string', description: 'Font name (e.g., "Arial", "Times New Roman")' } } }, fill: { type: 'object', description: 'Background fill options', properties: { color: { type: 'string', description: 'Background color in ARGB format (e.g., "FFFFFF00" for yellow)' }, pattern: { type: 'string', description: 'Fill pattern (default: "solid")' } } }, border: { type: 'object', description: 'Border styling options', properties: { style: { type: 'string', description: 'Border style: thin, medium, thick, dotted, dashed' }, color: { type: 'string', description: 'Border color in ARGB format (e.g., "FF000000" for black)' }, top: { type: 'boolean', description: 'Apply border to top (default: true)' }, bottom: { type: 'boolean', description: 'Apply border to bottom (default: true)' }, left: { type: 'boolean', description: 'Apply border to left (default: true)' }, right: { type: 'boolean', description: 'Apply border to right (default: true)' } } }, alignment: { type: 'object', description: 'Text alignment options', properties: { horizontal: { type: 'string', description: 'Horizontal alignment: left, center, right, justify' }, vertical: { type: 'string', description: 'Vertical alignment: top, middle, bottom' }, wrapText: { type: 'boolean', description: 'Wrap text within cell' }, textRotation: { type: 'number', description: 'Text rotation angle in degrees' } } }, numberFormat: { type: 'string', description: 'Number format (e.g., "$#,##0.00", "0.00%", "mm/dd/yyyy")' } } }, sheet: { type: 'string', description: 'Sheet name (optional, defaults to first sheet)', }, }, required: ['filePath', 'range', 'styling'], }, }, { name: 'auto_fit_columns', description: 'Automatically adjust column widths to fit content in Excel files', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the Excel file (.xlsx or .xls)', }, sheet: { type: 'string', description: 'Sheet name (optional, defaults to all sheets)', }, columns: { type: 'array', description: 'Specific columns to auto-fit (optional, defaults to all columns). Can be column letters (e.g., ["A", "B"]) or numbers (e.g., [1, 2])', items: { oneOf: [ { type: 'string', description: 'Column letter (e.g., "A", "B", "C")' }, { type: 'number', description: 'Column number (1-based, e.g., 1, 2, 3)' } ] } }, minWidth: { type: 'number', description: 'Minimum column width (default: 10)', default: 10 }, maxWidth: { type: 'number', description: 'Maximum column width (default: 60)', default: 60 }, padding: { type: 'number', description: 'Extra padding to add to calculated width (default: 2)', default: 2 } }, required: ['filePath'], }, }, // AI-Powered Tools { name: 'evaluate_formula', description: 'Evaluate an Excel formula with given context', inputSchema: { type: 'object', properties: { formula: { type: 'string', description: 'Excel formula to evaluate (e.g., "=SUM(A1:A10)", "=VLOOKUP(B2,C:D,2,FALSE)")', }, context: { type: 'object', description: 'Cell values and ranges for formula evaluation (optional)', additionalProperties: true, }, }, required: ['formula'], }, }, { name: 'parse_natural_language', description: 'Convert natural language to Excel formula or command', inputSchema: { type: 'object', properties: { query: { type: 'string', description: 'Natural language query (e.g., "sum all sales", "find duplicates", "average by category")', }, filePath: { type: 'string', description: 'Path to file for context (optional)', }, provider: { type: 'string', description: 'Preferred AI provider: anthropic, openai, deepseek, gemini, or local (optional)', enum: ['anthropic', 'openai', 'deepseek', 'gemini', 'local'], }, }, required: ['query'], }, }, { name: 'explain_formula', description: 'Explain what an Excel formula does in plain English', inputSchema: { type: 'object', properties: { formula: { type: 'string', description: 'Excel formula to explain (e.g., "=VLOOKUP(A2,B:C,2,FALSE)")', }, provider: { type: 'string', description: 'Preferred AI provider: anthropic, openai, deepseek, gemini, or local (optional)', enum: ['anthropic', 'openai', 'deepseek', 'gemini', 'local'], }, }, required: ['formula'], }, }, { name: 'ai_provider_status', description: 'Check status of available AI providers', inputSchema: { type: 'object', properties: {}, }, }, { name: 'smart_data_analysis', description: 'AI-powered analysis suggestions for your data', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file to analyze', }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)', }, provider: { type: 'string', description: 'Preferred AI provider: anthropic, openai, deepseek, gemini, or local (optional)', enum: ['anthropic', 'openai', 'deepseek', 'gemini', 'local'], }, }, required: ['filePath'], }, }, // Validation Tools (legacy - to be refactored) { name: 'validate_data_consistency', description: 'Cross-validate data integrity across related files', inputSchema: { type: 'object', properties: { primaryFile: { type: 'string', description: 'Path to the primary data file to validate' }, referenceFiles: { type: 'array', items: { type: 'string' }, description: 'Array of reference file paths for validation' }, validationRules: { type: 'array', items: { type: 'string', enum: ['referential_integrity', 'data_completeness', 'value_ranges'] }, description: 'Specific validation rules to apply (optional, defaults to all)' }, keyColumns: { type: 'array', items: { type: 'string' }, description: 'Specific columns to validate for referential integrity (optional)' }, autoDetectRelationships: { type: 'boolean', description: 'Automatically detect column relationships (default: true)' }, tolerance: { type: 'number', description: 'Tolerance for numeric validations (default: 0.01)' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' }, reportFormat: { type: 'string', enum: ['summary', 'detailed'], description: 'Format of validation report (default: detailed)' } }, required: ['primaryFile', 'referenceFiles'] } }, // Bulk Operations Tools (legacy - to be refactored) { name: 'bulk_aggregate_multi_files', description: 'Aggregate same column across multiple files in parallel', inputSchema: { type: 'object', properties: { filePaths: { type: 'array', items: { type: 'string' }, description: 'Array of file paths to process' }, column: { type: 'string', description: 'Column name or index (0-based) to aggregate' }, operation: { type: 'string', enum: ['sum', 'average', 'count', 'min', 'max'], description: 'Aggregation operation' }, consolidate: { type: 'boolean', description: 'Whether to return consolidated result or per-file breakdown (default: true)' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' }, filters: { type: 'array', description: 'Optional filters to apply before aggregation', items: { type: 'object', properties: { column: { type: 'string' }, condition: { type: 'string', enum: ['equals', 'contains', 'greater_than', 'less_than', 'not_equals'] }, value: { type: ['string', 'number'] } }, required: ['column', 'condition', 'value'] } } }, required: ['filePaths', 'column', 'operation'] } }, { name: 'bulk_filter_multi_files', description: 'Filter data across multiple files with optional export', inputSchema: { type: 'object', properties: { filePaths: { type: 'array', items: { type: 'string' }, description: 'Array of file paths to process' }, filters: { type: 'array', description: 'Filters to apply to the data', items: { type: 'object', properties: { column: { type: 'string' }, condition: { type: 'string', enum: ['equals', 'contains', 'greater_than', 'less_than', 'not_equals'] }, value: { type: ['string', 'number'] } }, required: ['column', 'condition', 'value'] } }, outputMode: { type: 'string', enum: ['count', 'export', 'summary'], description: 'How to return results: count only, export to file, or summary with counts' }, outputPath: { type: 'string', description: 'Output file path (required when outputMode is "export")' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' } }, required: ['filePaths', 'filters', 'outputMode'] } }, // Excel Workflow Tools { name: 'find_duplicates', description: 'Find and manage duplicate rows in Excel/CSV files with multiple strategies', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file' }, columns: { type: 'array', items: { type: 'string' }, description: 'Columns to check for duplicates (empty = all columns)' }, action: { type: 'string', enum: ['highlight', 'remove', 'export_duplicates', 'report_only'], description: 'What to do with duplicates (default: report_only)' }, keepFirst: { type: 'boolean', description: 'Keep first occurrence when removing (default: true)' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' } }, required: ['filePath'] } }, { name: 'data_cleaner', description: 'Batch data cleaning operations with intelligent detection of common data quality issues', inputSchema: { type: 'object', properties: { filePath: { type: 'string', description: 'Path to the CSV or Excel file' }, operations: { type: 'array', items: { type: 'string', enum: [ 'trim_whitespace', 'fix_dates', 'standardize_numbers', 'remove_empty_rows', 'standardize_phone_formats', 'standardize_names', 'remove_special_chars', 'fix_currency' ] }, description: 'Array of cleaning operations to apply' }, preview: { type: 'boolean', description: 'Show preview before applying changes (default: false)' }, sheet: { type: 'string', description: 'Sheet name for Excel files (optional)' } }, required: ['filePath'] } }, { name: 'vlookup_helper', description: 'Intelligent VLOOKUP setup and execution with error handling and fuzzy matching', inputSchema: { type: 'object', properties: { sourceFile: { type: 'string', description: 'File with data that needs lookup values' }, lookupFile: { type: 'string', description: 'File to lookup values from' }, lookupColumn: { type: 'string', description: 'Column name or index to match on' }, returnColumns: { type: 'array', items: { type: 'string' }, description: 'Columns to return from lookup table (empty = all except lookup column)' }, fuzzyMatch: { type: 'boolean', description: 'Enable fuzzy string matching for lookups (default: false)' }, handleErrors: { type: 'boolean', description: 'Auto-handle #N/A errors with fallbacks (default: true)' }, sourceSheet: { type: 'string', description: 'Sheet name for source Excel file (optional)' }, lookupSheet: { type: 'string', description: 'Sheet name for lookup Excel file (optional)' } }, required: ['sourceFile', 'lookupFile', 'lookupColumn'] } }, ], }));