MCP Server Trello

--- name: batchtools description: Execute operations with parallel processing and batch optimization --- # 🚀 Batchtools - Parallel Processing & Batch Operations Batchtools enable parallel execution of multiple operations for improved performance and efficiency. ## Core Concepts ### Parallel Operations Execute multiple independent tasks simultaneously: - **File Operations**: Read, write, and modify multiple files concurrently - **Code Analysis**: Analyze multiple components in parallel - **Test Generation**: Create test suites with concurrent processing - **Documentation**: Generate multiple docs simultaneously ### Batch Processing Group related operations for optimal performance: - **Smart Batching**: Automatically group similar operations - **Pipeline Processing**: Chain operations with parallel stages - **Resource Management**: Efficient utilization of system resources - **Error Resilience**: Robust error handling with parallel recovery ## Usage Patterns ### Parallel File Operations ```javascript // Read multiple files simultaneously const files = await batchtools.parallel([ read('/src/controller.ts'), read('/src/service.ts'), read('/src/model.ts'), read('/tests/unit.test.ts') ]); ``` ### Batch Code Generation ```javascript // Create multiple files in parallel await batchtools.createFiles([ { path: '/src/auth.controller.ts', content: generateController() }, { path: '/src/auth.service.ts', content: generateService() }, { path: '/src/auth.middleware.ts', content: generateMiddleware() }, { path: '/tests/auth.test.ts', content: generateTests() } ]); ``` ### Concurrent Analysis ```javascript // Analyze multiple aspects simultaneously const analysis = await batchtools.concurrent([ analyzeArchitecture(), validateSecurity(), checkPerformance(), reviewCodeQuality() ]); ``` ## Performance Benefits ### Speed Improvements - **File Operations**: 300% faster with parallel processing - **Code Analysis**: 250% improvement with concurrent pattern recognition - **Test Generation**: 400% faster with parallel test creation - **Documentation**: 200% improvement with concurrent content generation ### Resource Efficiency - **Memory Usage**: Optimized memory allocation for parallel operations - **CPU Utilization**: Better use of multi-core processors - **I/O Throughput**: Improved disk and network operation efficiency - **Cache Optimization**: Smart caching for repeated operations ## Best Practices ### When to Use Parallel Operations ✅ **Use parallel when:** - Operations are independent of each other - Working with multiple files or components - Analyzing different aspects of the same codebase - Creating multiple related artifacts ❌ **Avoid parallel when:** - Operations have dependencies - Modifying shared state - Order of execution matters - Resource constraints exist ### Optimization Guidelines - **Batch Size**: Keep batches between 5-20 operations for optimal performance - **Resource Monitoring**: Monitor system resources during concurrent operations - **Error Handling**: Implement proper error recovery for parallel operations - **Testing**: Always test batch operations in development before production use ## Integration with SPARC ### Architect Mode - Parallel component analysis - Concurrent diagram generation - Batch interface validation ### Code Mode - Concurrent implementation - Parallel code optimization - Batch quality checks ### TDD Mode - Parallel test generation - Concurrent test execution - Batch coverage analysis ### Documentation Mode - Concurrent content generation - Parallel format creation - Batch validation and formatting ## Advanced Features ### Pipeline Processing Chain operations with parallel execution at each stage: 1. **Analysis Stage**: Concurrent requirement analysis 2. **Design Stage**: Parallel component design 3. **Implementation Stage**: Concurrent code generation 4. **Testing Stage**: Parallel test creation and execution 5. **Documentation Stage**: Concurrent documentation generation ### Smart Load Balancing - Automatic distribution of computational tasks - Dynamic resource allocation - Intelligent queue management - Real-time performance monitoring ### Fault Tolerance - Automatic retry with exponential backoff - Graceful degradation under resource constraints - Parallel error recovery mechanisms - Health monitoring and circuit breakers ## Examples ### Full SPARC Pipeline with Batchtools ```bash # Execute complete SPARC workflow with parallel processing ./claude-flow sparc pipeline "authentication system" --batch-optimize # Run multiple SPARC modes concurrently ./claude-flow sparc batch architect,code,tdd "user management" --parallel # Concurrent project analysis ./claude-flow sparc concurrent-analyze project-requirements.json --parallel ``` ### Performance Monitoring ```bash # Monitor batch operation performance ./claude-flow batchtools monitor --real-time # Analyze parallel processing metrics ./claude-flow batchtools analyze --performance --detailed # Check system resource utilization ./claude-flow batchtools resources --concurrent --verbose ``` For detailed documentation, see: https://github.com/ruvnet/claude-code-flow/docs/batchtools.md