batch_process_csv

Process multiple CSV files simultaneously with consistent filtering and column selection to achieve significant token savings compared to individual file processing.

Instructions

Process multiple CSV files in a single call with consistent filtering. Achieves 80% token savings for multiple files vs individual calls.

Input Schema

TableJSON Schema

Name	Required	Description
`file_paths`	Yes	Paths to CSV files (max 5)
`filter_expr`	No	Filter expression applied to all files
`columns`	No	Columns to select from all files
`limit`	No	Maximum rows per file
`aggregate`	No	If true, combine results from all files into aggregated summary

Implementation Reference

src/index.ts:297-385 (handler)

Main handler for batch_process_csv tool. Processes multiple CSV files (max 5) with consistent filtering and optional aggregation. Calls executeProcessCSV for each file, aggregates results, and returns token metrics with estimated 80% savings compared to individual file calls.

private async batchProcessCSV(args: {
  file_paths: string[];
  filter_expr?: string;
  columns?: string[];
  limit?: number;
  aggregate?: boolean;
}): Promise<{ content: any[] }> {
  const startTime = Date.now();
  const filePaths = args.file_paths.slice(0, 5); // Max 5 files
  const results: any[] = [];

  for (const filePath of filePaths) {
    try {
      const singleArgs = {
        file_path: filePath,
        filter_expr: args.filter_expr,
        columns: args.columns,
        limit: args.limit || 100,
        offset: 0,
        response_format: 'summary'
      };

      const innerResult = await this.executeProcessCSV(singleArgs);
      results.push({
        file_path: filePath,
        success: true,
        result: innerResult.result
      });
    } catch (error: any) {
      results.push({
        file_path: filePath,
        success: false,
        error: error.message
      });
    }
  }

  const processingTime = Date.now() - startTime;
  const successful = results.filter(r => r.success);

  let finalResult: any;

  if (args.aggregate) {
    // Aggregate results
    finalResult = {
      success: true,
      batch_summary: {
        total_files: filePaths.length,
        successful: successful.length,
        failed: results.length - successful.length,
        processing_time_ms: processingTime
      },
      aggregated_data: {
        total_rows: successful.reduce((sum, r) => sum + (r.result?.total_rows || 0), 0),
        filtered_rows: successful.reduce((sum, r) => sum + (r.result?.filtered_rows || 0), 0),
        returned_rows: successful.reduce((sum, r) => sum + (r.result?.returned_rows || 0), 0),
      },
      per_file_results: results
    };
  } else {
    finalResult = {
      success: true,
      total_files: filePaths.length,
      successful: successful.length,
      failed: results.length - successful.length,
      processing_time_ms: processingTime,
      results
    };
  }

  return {
    content: [{
      type: 'text',
      text: JSON.stringify({
        ...finalResult,
        token_metrics: {
          input_tokens: this.estimateTokens(JSON.stringify(args)),
          output_tokens: this.estimateTokens(JSON.stringify(finalResult)),
          result_size_bytes: this.getResultSizeBytes(finalResult),
          processing_time_ms: processingTime,
          cached: false,
          cache_hit: false,
          estimated_savings_percent: '80',
          note: 'Batch processing saves ~80% vs individual file calls'
        }
      }, null, 2)
    }]
  };
}

src/index.ts:702-716 (schema)

Tool registration schema defining batch_process_csv input parameters: file_paths (array of max 5), filter_expr, columns, limit, and aggregate flag. Includes description emphasizing 80% token savings for multiple files.

name: 'batch_process_csv',
title: 'Batch Process Multiple CSV Files',
description: 'Process multiple CSV files in a single call with consistent filtering. Achieves 80% token savings for multiple files vs individual calls.',
inputSchema: {
  type: 'object',
  properties: {
    file_paths: { type: 'array', items: { type: 'string' }, description: 'Paths to CSV files (max 5)', minItems: 1, maxItems: 5 },
    filter_expr: { type: 'string', description: 'Filter expression applied to all files' },
    columns: { type: 'array', items: { type: 'string' }, description: 'Columns to select from all files' },
    limit: { type: 'number', default: 100, description: 'Maximum rows per file' },
    aggregate: { type: 'boolean', default: false, description: 'If true, combine results from all files into aggregated summary' },
  },
  required: ['file_paths'],
  $schema: 'https://json-schema.org/draft/2020-12/schema',
},

src/index.ts:104-105 (registration)
Switch case routing tool call 'batch_process_csv' to the batchProcessCSV handler method.
```
case 'batch_process_csv':
  return await this.batchProcessCSV(args as any);
```

src/index.ts:724-850 (helper)

executeProcessCSV helper method that performs actual CSV processing using Python/pandas. Supports caching, filtering, column selection, aggregation, and pagination. Generates dynamic Python code for each file.

  private async executeProcessCSV(args: any): Promise<ProcessResult> {
    const cacheKey = ResultCache.generateKey('process_csv', args);
    const cached = this.cache.get(cacheKey);

    if (cached) {
      const cachedResult = cached.result as ProcessResult;
      // Don't return cached errors
      if (!cachedResult.success) {
        return cachedResult;
      }
      return {
        success: true,
        result: {
          ...(cachedResult.result || {}),
          cached: true,
          cache_hit: true
        },
        token_metrics: {
          input_tokens: cachedResult.token_metrics?.input_tokens || 0,
          output_tokens: cachedResult.token_metrics?.output_tokens || 0,
          result_size_bytes: cachedResult.token_metrics?.result_size_bytes || 0,
          processing_time_ms: cachedResult.token_metrics?.processing_time_ms || 0,
          cached: true,
          cache_hit: true,
          estimated_savings_percent: '90'
        }
      };
    }

    const startTime = Date.now();
    const offset = args.offset || 0;
    const limit = args.limit || 100;
    const aggregateBy = args.aggregate_by || '';
    const aggFunc = args.agg_func || 'mean';

    const pythonCode = `
import pandas as pd
import json
import sys

try:
    # Read CSV
    df = pd.read_csv('${args.file_path}')
    total_rows = len(df)

    # Apply filters
    if '${args.filter_expr || ''}':
        df = df.query('${args.filter_expr}')

    filtered_count = len(df)

    # Select columns
    if ${JSON.stringify(args.columns || [])}:
        df = df[${JSON.stringify(args.columns)}]

    # Handle aggregation (groupby)
    if '${aggregateBy}':
        agg_func = '${aggFunc}'
        grouped = df.groupby('${aggregateBy}', dropna=False)

        result = {
            'total_rows': total_rows,
            'filtered_rows': filtered_count,
            'grouped': True,
            'group_by_column': '${aggregateBy}',
            'aggregation_function': agg_func,
            'num_groups': len(grouped),
            'groups': {}
        }

        for group_name, group_df in grouped:
            group_stats = {}
            numeric_cols = group_df.select_dtypes(include=['number']).columns

            for col in numeric_cols:
                if agg_func == 'count':
                    group_stats[col] = len(group_df[col].dropna())
                elif agg_func == 'sum':
                    group_stats[col] = float(group_df[col].sum())
                elif agg_func == 'mean':
                    group_stats[col] = float(group_df[col].mean())
                elif agg_func == 'min':
                    group_stats[col] = float(group_df[col].min())
                elif agg_func == 'max':
                    group_stats[col] = float(group_df[col].max())

            result['groups'][str(group_name)] = {
                'count': len(group_df),
                'stats': group_stats
            }

        print(json.dumps(result))
    else:
        # Apply offset and limit (pagination)
        df = df.iloc[${offset}:${offset + limit}]

        result = {
            'total_rows': total_rows,
            'filtered_rows': filtered_count,
            'returned_rows': len(df),
            'offset': ${offset},
            'limit': ${limit},
            'has_more': ${offset + limit} < filtered_count,
            'columns': list(df.columns),
        }

        if '${args.response_format || 'summary'}' == 'summary':
            numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
            if len(numeric_cols) > 0:
                result['data_summary'] = df[numeric_cols].describe().to_dict()
            result['sample_data'] = df.head(5).to_dict('records')
        else:
            result['data'] = df.to_dict('records')

        print(json.dumps(result))

except Exception as e:
    print(json.dumps({
        'success': False,
        'error': str(e)
    }))
`;

    const result = await this.executePython(pythonCode);
    const processingTime = Date.now() - startTime;

    // Only cache successful results

src/index.ts:129-138 (helper)

Utility methods estimateTokens and getResultSizeBytes used by batch_process_csv to calculate token metrics and result sizes for performance tracking.

private estimateTokens(text: string): number {
  return Math.ceil(text.length / 4);
}

/**
 * Calculate result size in bytes
 */
private getResultSizeBytes(result: any): number {
  return JSON.stringify(result).length;
}

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