Stata-MCP

# Changelog (Auto Generated by Claude Code) <details> <summary>Click to expand v1.11.0 details</summary> ## [1.11.0] - 2025-10-20 ### Added - **Claude Code Client Mode**: Added dedicated support for Claude Code client environment - New `-c/--client` CLI option for Claude Code client mode - Automatic detection and configuration for Claude Code environment - Enhanced working directory handling for client-specific workflows - Optimized MCP server integration for Claude Code platform ### Changed - **Code Architecture**: Major refactoring to improve modularity and maintainability - Extracted MCP server initialization and tools from `__init__.py` to dedicated `mcp_servers.py` module - Updated main.py import path to use new modular structure - Streamlined package initialization with better separation of concerns - Improved code organization for enhanced development experience ### Changed - **Dependency Management**: Optimized dependency structure for cleaner installation - Removed python-dotenv from active dependencies as it's no longer required - Updated uv.lock file to reflect dependency changes - Improved CLI import structure for better performance - Enhanced version import using importlib.metadata instead of package-level import ### Changed - **CLI Enhancements**: Improved command-line interface with new client mode support - Restructured CLI imports for better loading performance - Added lazy loading patterns for non-critical components - Enhanced error handling and user feedback - Better separation between different CLI modes (agent, client, mcp) ### Technical - **Performance**: Enhanced startup performance through optimized imports and modular architecture - **Maintainability**: Improved code organization with clear separation between server initialization and package metadata - **Compatibility**: Better support for different client environments and use cases - **Dependencies**: Streamlined dependency list with removal of unnecessary python-dotenv requirement </details> <details> <summary>Click to expand v1.10.2 details</summary> ## [1.10.2] - 2025-10-17 ### Added - **AgentRunner Evaluation Method**: Complete evaluation workflow implementation in AgentRunner class - New `evaluate()` method for seamless ScoreModel integration with automatic task execution and scoring - Type conversion handling for processer data (List[Dict] to String format) for ScoreModel compatibility - Optional result display with `is_display` parameter for immediate feedback and debugging - Automatic conversation history extraction and formatting for evaluation purposes - Direct parameter passing and type-safe integration between AgentRunner and ScoreModel - **Enhanced Evaluation Integration**: Streamlined ScoreModel connection with agent execution - Automatic task execution and result processing in a single method call - Built-in error handling and validation throughout the evaluation pipeline - Comprehensive fallback mechanisms for robust evaluation operations - Simplified evaluation workflow with reduced boilerplate code ### Changed - **Developer Experience**: Improved evaluation workflow simplicity and usability - Single `evaluate()` call replaces multi-step evaluation process - Automatic handling of type conversions between AgentRunner and ScoreModel components - Optional display functionality for real-time monitoring and debugging - Enhanced error reporting and validation for evaluation scenarios - **Type Compatibility**: Enhanced data flow between evaluation components - Robust integration between different data structures (List[Dict] to String conversion) - Preserved conversation structure and context during type conversion - Maintained data integrity throughout the evaluation pipeline - Type-safe parameter handling for reliable evaluation workflows ### Technical - **Code Architecture**: Enhanced AgentRunner with complete evaluation capabilities - Clean separation between task execution and evaluation logic - Modular design allowing for easy customization and extension - Comprehensive type annotations and documentation for evaluation methods - Improved error handling and validation throughout the evaluation framework - **Backward Compatibility**: Maintained full compatibility with existing evaluation workflows - All existing AgentRunner functionality remains unchanged - New evaluate() method is additive without disrupting existing code - Seamless integration with existing ScoreModel evaluation framework - Zero impact on current evaluation processes and tools </details> <details> <summary>Click to expand v1.10.1 details</summary> ## [1.10.1] - 2025-10-16 ### Added - **AgentRunner Class**: New comprehensive agent management system for simplified LLM evaluation workflows - OpenAI-compatible API integration with support for multiple providers (OpenAI, DeepSeek, etc.) - `get_processer()` static method for automatic conversation history extraction from agent results - `get_final_result()` static method for extracting final answers from agent outputs - Built-in error handling and graceful fallback mechanisms for robust operation - Full type annotations and comprehensive documentation with practical examples - **Comprehensive Testing Framework**: Complete test suite for AgentRunner functionality validation - Mock-based testing system for reliable validation without API dependencies - Real API testing with automatic fallback to mock tests when credentials unavailable - Comprehensive error handling validation and conversation structure verification - Integrity checks for method outputs and data format validation - Complete test coverage in `tar.py` with both mock and real API scenarios - **Enhanced Documentation**: Updated evaluation guide with AgentRunner integration examples - Step-by-step AgentRunner usage instructions with practical Stata analysis examples - Complete code examples for real-world evaluation scenarios including auto dataset analysis - Batch evaluation workflows for processing multiple tasks efficiently - Custom evaluation criteria and metrics implementation examples - Environment setup guides for different API providers (OpenAI, DeepSeek) ### Changed - **Module Organization**: Enhanced evaluate module structure with better component integration - AgentRunner now properly exported in `evaluate.__init__.py` for direct imports - Cleaner module structure with improved separation of concerns - Better discoverability of evaluation components and enhanced import experience - Updated `Evaluation.md` documentation with comprehensive AgentRunner usage examples - **Developer Experience**: Streamlined evaluation workflow with simplified task execution - One-click conversation extraction and result processing through AgentRunner methods - Reduced boilerplate code for common evaluation scenarios and use cases - Better error messages and debugging information for improved development experience - Enhanced integration between agents and evaluation tools - **Evaluation Framework**: Improved evaluation workflow with better tool integration - Seamless integration between AgentRunner and existing ScoreModel functionality - Enhanced support for OpenAI Agents result processing and extraction - Better handling of complex conversation structures with multi-turn interactions - Improved role detection and content extraction for diverse agent response formats ### Fixed - **Conversation Processing**: Enhanced handling of complex conversation structures in agent outputs - Better support for multi-turn conversations with tool interactions and role switching - Enhanced role detection and content extraction from various agent response formats - More robust parsing of agent response data with improved error resilience - Better handling of edge cases in conversation structure processing - **Testing Reliability**: Enhanced test stability and comprehensive error handling - Improved mock data structure for more realistic testing scenarios - Better exception handling in test environments with detailed error reporting - More comprehensive validation of method outputs and data integrity - Enhanced test reliability through better error recovery mechanisms ### Technical - **Dependencies**: No new dependencies added for AgentRunner functionality - AgentRunner uses existing project dependencies (openai-agents, openai, langchain) - Lightweight implementation with minimal performance impact - Backward compatibility maintained for all existing features and workflows - **Code Architecture**: Enhanced evaluation module with improved component integration - Clean separation between agent execution and result processing logic - Modular design allows for easy extension and customization - Standardized evaluation criteria based on professional statistical standards - Improved error handling and fallback mechanisms throughout the framework </details> <details> <summary>Click to expand v1.10.0 details</summary> ## [1.10.0] - 2025-10-14 ### Added - **LLM Evaluation Module Framework**: Comprehensive evaluation system for Large Language Model performance assessment - New `evaluate` package with complete module structure (`src/stata_mcp/evaluate/`) - `_model.py` providing structured assessment framework and evaluation criteria - `advice.py` for evaluation advice generation and result analysis - `score_it.py` for automated LLM performance scoring and assessment - Full type annotations and comprehensive documentation for evaluation use - **ScoreModel Evaluation System**: Automated LLM performance assessment framework - Task completion accuracy evaluation based on reference answers - Response quality assessment against expected outcomes - Process analysis for LLM reasoning quality evaluation - Historical message processing for context assessment - Quantitative metrics for model comparison and benchmarking - **Structured Evaluation Framework**: Systematic approach to LLM performance evaluation - Task definition and reference answer management system - Process evaluation for analyzing LLM reasoning chains - Final answer scoring and validation mechanisms - Configurable evaluation parameters for different use cases - Extensible framework for custom evaluation criteria - **Research Assessment Tools**: Built-in evaluation capabilities for AI research workflows - Standardized evaluation methodology for reproducible research - Benchmarking framework for model performance comparison - Reference answer management for accuracy assessment - Process analysis capabilities for reasoning evaluation ### Changed - **Enhanced Research Capabilities**: Integrated LLM evaluation into existing Stata-MCP functionality - Evaluation framework works seamlessly with current research workflows - Optional enhancement for AI performance assessment without disrupting existing functionality - Flexible configuration for different evaluation scenarios and research requirements - Multi-language support for evaluation feedback and assessment results - **Documentation Updates**: Enhanced user documentation with LLM evaluation features - Updated main README with LLM evaluation module announcement - Synchronized Chinese README with new evaluation capabilities - Added Evaluation.md documentation for LLM assessment workflows - Enhanced quick start guides with evaluation setup examples ### Technical - **Code Architecture**: New evaluation module with clean separation of concerns - Modular design allows for easy extension and customization - Standardized evaluation criteria based on professional statistical standards - Configurable scoring parameters for different assessment requirements - Professional statistical standards integration for academic use - **Dependencies**: No new dependencies added for evaluation functionality - Evaluation module uses existing project dependencies - Lightweight implementation with minimal performance impact - Backward compatibility maintained for all existing features </details> <details> <summary>Click to expand v1.9.1 details</summary> ## [1.9.1] - 2025-10-12 ### Removed - **WebUI Module**: Completely removed Flask-based web interface and all related functionality - Removed Flask dependency and all web-related dependencies from `pyproject.toml` - Deleted webui module (`src/stata_mcp/webui/`) including templates, static assets, and utilities - Removed `--webui` CLI argument and related webui startup functionality - Deleted `WEBUI.md` documentation file - **Config Module**: Discontinued TOML-based configuration system - Removed config module (`src/stata_mcp/config/`) and all configuration management logic - Deleted `Configuration.md` documentation file - Removed `example.toml` configuration template - Simplified project to use environment variable-based configuration only ### Changed - **Project Structure**: Major simplification to focus on core MCP functionality - Streamlined project structure by removing 17 files and 2299 lines of code - Reduced dependency footprint for faster installation and smaller package size - Improved startup performance by eliminating complex configuration loading - Enhanced code maintainability through reduced complexity - **Dependency Management**: Optimized dependency list for lightweight installation - Removed Flask and web-related dependencies - Commented out optional jupyter-related dependencies not currently used - Maintained all core dependencies for Stata-MCP functionality - Updated `uv.lock` to reflect new dependency structure - **Configuration Simplification**: Streamlined configuration approach - Now relies solely on environment variables for configuration - Maintained dotenv loading for essential settings - Removed complex TOML parsing and validation logic - Better defaults for improved out-of-the-box experience ### Technical - **Code Quality**: Improved maintainability and focus - Reduced attack surface by removing web interface components - Fewer potential points of failure in initialization - Cleaner separation of concerns between CLI and core functionality - Better alignment with MCP protocol's primary use case - **Performance**: Enhanced startup and runtime performance - Faster initialization due to reduced module loading - Lower memory footprint from fewer loaded dependencies - Simplified error handling paths - Improved reliability through reduced complexity ### Migration Notes - **WebUI Users**: Web interface no longer available - use CLI interface instead - **Config File Users**: TOML configuration no longer supported - migrate to environment variables - **Minimal Impact**: Most users unaffected as they were already using CLI-based workflow - **Simple Migration**: Straightforward migration path for affected users </details> <details> <summary>Click to expand v1.9.0 details</summary> ## [1.9.0] - 2025-10-11 ### Added - **Agent as Tool Framework**: Revolutionary multi-agent workflow support for Stata analysis - New `StataAgent` class with comprehensive ReAct (Reasoning-Action-Observation) framework - Professional Stata Data Analysis Expert role with economic research assistant capabilities - Seamless integration as a tool within other AI agents for complex workflows - Default comprehensive instructions covering data understanding, code generation, execution, and results interpretation - Configurable tool descriptions with clear capabilities and input/output specifications - **Multi-Model Provider Support**: Enhanced compatibility with various LLM providers - Extended type hints to support `OpenAIChatCompletionsModel | Model` union types - `set_model()` utility function for easy configuration of alternative providers (DeepSeek, etc.) - Native support for OpenAI ChatCompletionsModel with fallback to generic Model interface - Improved IDE support and code completion through enhanced type annotations - **Comprehensive Documentation Suite**: Complete bilingual documentation for Agent as Tool functionality - Detailed `agent_as_tool.md` guide with practical examples and use cases - Quick start guide for basic usage and advanced configuration examples - Integration patterns with existing agent frameworks (OpenAI Agents, LangChain) - Multi-provider setup examples with DeepSeek and other OpenAI-compatible models - **Enhanced README Integration**: Improved user onboarding with Agent as Tool examples - New "Agent as Tool" section in main README with working code examples - Synchronized Chinese README with all new features and examples - Updated news sections highlighting new multi-agent capabilities - Clear navigation to detailed documentation and quick start guides ### Changed - **Agent Architecture Enhancements**: Modular and flexible agent design - Clean separation between agent logic and tool integration through `as_tool` property - Flexible MCP server configuration with environment variable support - Enhanced tracing control for performance optimization and debugging - Configurable agent behavior through custom instructions, models, and tools - **Developer Experience Improvements**: Enhanced usability and flexibility - Support for custom agent names, instructions, and tool descriptions - Adjustable `max_turns` parameter for complex analysis tasks - Configurable `DISABLE_TRACING` for performance optimization - Better error handling and connection management for MCP servers - **Type Safety and IDE Support**: Improved development experience - Enhanced type annotations throughout the agent framework - Support for union types (`OpenAIChatCompletionsModel | Model`) - Better IDE support with improved code completion and error detection - Reduced runtime errors through comprehensive type checking ### Technical - **New Dependencies**: Added `agents` library for Agent as Tool functionality - Support for OpenAI Agents framework with seamless integration - Enhanced MCP server configuration and management - Improved error handling and connection stability - **Code Organization**: Enhanced module structure for agent functionality - New `agent_as_tool` module with `StataAgent` and `set_model` utilities - Clean separation of concerns between agent logic and tool integration - Improved maintainability and extensibility for future agent features - **Configuration Flexibility**: Enhanced customization options - Environment variable support for Stata CLI configuration - Customizable agent instructions and tool descriptions - Flexible model provider configuration with easy switching between providers </details> <details> <summary>Click to expand v1.8.2 details</summary> ## [1.8.2] - 2025-10-10 ### Added - **URL-based DTA File Reading**: Implemented support for reading Stata DTA files directly from HTTP/HTTPS URLs - Added URL detection and validation in DtaDataInfo class - Implemented memory-efficient reading using BytesIO for optimal performance - Support for both `http://` and `https://` protocols with comprehensive error handling - Seamless integration with existing local file functionality - Automatic URL format validation and file extension verification ### Changed - **Enhanced DtaDataInfo Architecture**: Extended DtaDataInfo class to support both local and remote files - Unified `_read_data()` method handles both file sources without code duplication - Intelligent file path detection (URL vs local path) for appropriate processing - Streamlined DataInfoBase class by removing unnecessary abstract method `_read_data_from_url()` - Cleaner separation of concerns between local and remote operations - **Documentation Updates**: Enhanced API documentation and examples - Updated docstring examples in `__init__.py` with comprehensive output format examples - Added clear demonstration of remote data analysis capabilities - Improved error handling documentation for network operations ### Fixed - **URL Handling Conflicts**: Resolved Path object conflicts when processing URLs as file paths - Fixed URL validation to properly parse and validate HTTP/HTTPS URLs - Corrected file extension checking for remote files using URL path parsing - Improved error messages to provide clearer feedback for URL-related issues - **Network Error Handling**: Enhanced error handling for network operations - Added comprehensive timeout configurations and status code validation - Improved error reporting for network failures, invalid URLs, and file format issues - Better exception handling with informative error messages for troubleshooting ### Technical - **New Dependencies**: Added `requests` library for HTTP operations - Robust HTTP client with built-in error handling and retry mechanisms - Efficient content handling for binary DTA files with proper encoding - Support for HTTP/HTTPS protocols with TLS security - **Memory Optimization**: Implemented memory-efficient data processing - Direct loading into BytesIO eliminates temporary file overhead - Streaming content handling for large files with optimal memory usage - Zero intermediate file operations for URL-based data access </details> <details> <summary>Click to expand v1.8.1 details</summary> ## [1.8.1] - 2025-10-09 ### Fixed - **MCP Version Compatibility**: Enhanced MCP initialization logic for better version compatibility - Improved error handling for different MCP implementations (v1.16.0+) - Enhanced FastMCP initialization with fallback mechanisms - Better robustness when encountering validation errors - Streamlined version-specific initialization logic ### Changed - **Initialization Process**: Reorganized FastMCP initialization sequence - Prioritized MCP v1.16.0+ configuration with proper icon array format - Improved fallback error handling with non-config initialization - Enhanced user guidance for MCP version upgrades ### Technical - **Code Robustness**: Improved error handling and initialization reliability - **Version Support**: Better compatibility with MCP v1.16.0 and newer versions </details> <details> <summary>Click to expand v1.8.0 details</summary> ## [1.8.0] - 2025-10-09 ### Added - **Enhanced Data Info Support**: Added comprehensive multi-format data information functionality - New `CsvDataInfo` class for handling CSV file metadata and statistics - Enhanced `DtaDataInfo` class for Stata .dta file data information extraction - Improved `DataInfoBase` base class with kwargs support for extensibility - Enhanced `get_data_info` function with CSV and enhanced DTA file format support - Added save functionality with configurable output options - Implemented temporary directory management for data processing - Full type annotations and comprehensive documentation - **Enabled Data Info Tool**: Reactivated `get_data_info` functionality - Tool decorator re-enabled for production use - Support for multiple file formats: .dta, .csv, and Excel files - Improved error handling and user feedback - Enhanced data summary statistics and metadata extraction ### Changed - **MCP Version Upgrade**: Updated MCP dependency from v1.15.0 to v1.16.0 - Enhanced FastMCP initialization logic for better version compatibility - Improved error handling for different MCP implementations - Better robustness in initialization process - Streamlined dependency management ### Technical - **Code Architecture**: Improved data info module structure with base classes - **Type Safety**: Enhanced type annotations across data info functionality - **Module Organization**: Better separation of concerns in data processing modules </details> <details> <summary>Click to expand v1.7.4 details</summary> ## [1.7.4] - 2025-10-09 ### Fixed - **MCP Dependency Conflicts**: Resolved version conflicts in MCP dependency chain - Fixed compatibility issues with different MCP implementations - Enhanced system stability through streamlined dependency management - Improved error handling for dependency-related edge cases ### Technical - **Dependency Optimization**: Streamlined MCP dependencies for better stability - **Version Update**: Updated version from 1.7.3 to 1.7.4 </details> <details> <summary>Click to expand v1.7.3 details</summary> ## [1.7.3] - 2025-10-06 ### Added - **Enhanced Encoding Support**: Added configurable encoding parameters to dofile functions - `write_dofile` now supports optional `encoding` parameter (default: utf-8) - `append_dofile` enhanced with configurable encoding for read/write operations - Better support for international character sets including Chinese, Japanese, Korean - Maintains full backward compatibility with existing code ### Fixed - **Issue #18**: Resolved potential Chinese character encoding problems in dofile operations - Proactive fix for international character set support - Enables flexible encoding handling for various environments ### Technical - **Encoding Flexibility**: Improved dofile encoding handling without breaking changes - **Version Update**: Updated version from 1.7.2 to 1.7.3 </details> <details> <summary>Click to expand v1.7.2 details</summary> ## [1.7.2] - 2025-10-06 ### Added - **Agent Mode Support**: Added comprehensive agent mode functionality - New `stata-mcp --agent` command line option for interactive AI-driven analysis - `StataAgent` class with LangChain integration for AI-powered Stata operations - Support for GPT-5, DeepSeek, and other OpenAI-compatible models - ReAct (Reasoning + Acting) prompt template for enhanced AI reasoning - MultiServerMCPClient integration for seamless Stata command execution - Interactive workflow with data source and task input prompts - **Agent Examples**: Added complete agent implementation examples - LangChain and LangGraph integration examples - OpenAI-based agent implementation - Advanced prompt generation system for agent tasks - Comprehensive documentation and README files - **Agent Startup Script**: Added `agent.sh` automated startup script - Auto-detection of uv package manager with fallback to pip - Python 3.11+ version validation and environment checking - Interactive installation prompts for uv package manager - Automatic package installation and version validation - Seamless agent mode launch with proper error handling - Cross-platform compatibility with colored output for better UX ### Changed - **Jupyter Dependencies**: Removed unused Jupyter-related dependencies from `pyproject.toml` - Commented out `jupyter-client>=8.6.3` and `stata-kernel>=1.12.2` - Commented out `notebook>=7.4.5` and `jupyter>=1.1.1` - Streamlined installation process and reduced package size - No impact on core Stata-MCP functionality ### Technical - **Dependency Optimization**: Cleaned up unused dependencies for faster installation - **Agent Mode Integration**: Enhanced CLI with agent mode support via `-a/--agent` flag - **Version Update**: Updated version from 1.7.1 to 1.7.2 </details> <details> <summary>Click to expand v1.7.1 details</summary> ## [1.7.1] - 2025-10-05 ### Changed - **mk_dir Security**: Re-enabled `mk_dir` tool with enhanced security using pathvalidate library - Added comprehensive path validation and sanitization - Implemented secure directory creation with proper permissions (0o755) - Added detailed error handling for invalid paths and permission issues - Improved function documentation with comprehensive parameter descriptions ### Technical - **Dependencies**: Added `pathvalidate>=3.3.1` for secure path validation - **Version Update**: Updated version from 1.7.0 to 1.7.1 </details> <details> <summary>Click to expand v1.7.0 details</summary> ## [1.7.0] - 2025-10-4 ### Added - **AI-Assisted Research**: Added comprehensive AI-assisted empirical research report with latest findings - **Prompt Engineering**: Introduced comprehensive task prompt guide and examples for better AI interaction - **Template System**: Added prompt-generator template for standardized AI request formatting - **Research Documentation**: Added detailed research report on StataMCP usage for social science research - **Prompt Examples**: Added two practical examples for PromptGenerator usage - **Load Figure**: Added `load_figure` functionality for image handling - **Sandbox Infrastructure**: Added sandbox infrastructure for testing - **Main Entry Point**: Added `main.py` entry point for local development - **Multilingual Documentation**: Updated Chinese, French, and Spanish README files - **Agent Mode Support**: Added agent mode support documentation - **China Users Guide**: Added specialized documentation for China users - **LLM Integration Guide**: Added comprehensive LLM documentation for AI integration ### Changed - **MCP Protocol**: Upgraded MCP from version 1.14 to 1.15 for latest features - **Module Naming**: Renamed `StataFinder` to `stata_finder` for snake_case consistency - **Directory Structure**: Improved code organization with better directory structure - **Contributing Guide**: Updated CONTRIBUTING.md documentation - **Security Policy**: Enhanced security policy with comprehensive privacy disclaimer - **Git Standards**: Updated CLAUDE.md with git push restrictions and standards - **App Icon**: Updated app icon to higher resolution image - **Project Dependencies**: Updated project dependencies and lock files ### Disabled (Commented Out) - **Directory Creation**: `mk_dir` tool implemented but decorator commented out for safety considerations - **Data Info Function**: `get_data_info` tool implemented but decorator commented out ### Fixed - **ValueError Prevention**: Fixed default instructions setting to avoid ValueError - **macOS Compatibility**: Fixed errors in StataFinder.macos - **Citation Corrections**: Fixed citation mistakes in research documentation - **Environment Configuration**: Fixed environment name errors in configuration - **Debug Cleanup**: Removed leftover debug print statements ### Technical - **Code Architecture**: Improved code organization and maintainability - **Function Safety**: Temporarily disabled certain functions via decorator commenting for security - **Development Environment**: Enhanced development environment setup - **Data Info Stability**: Temporarily disabled `get_data_info` for stability considerations </details> <details> <summary>Click to expand v1.6.3 details</summary> ## [1.6.3] - 2025-09-12 ### Added - **MCP Resource Support**: Added `@mcp.resource` decorator for `help` function with URI `help://stata/{cmd}` - **AI Coding Ability Report**: Added comprehensive comparison chart of different AI models' Stata code generation capabilities in source documentation ### Changed - **Dependency Upgrade**: Upgraded `mcp[cli]` from `>=1.9.0` to `>=1.13.0` for latest MCP protocol features - **Version Updates**: Updated version numbers across all documentation files and CITATION.cff ### Technical - **Enhanced MCP Support**: Improved MCP protocol compatibility with resource URI support </details> <details> <summary>Click to expand v1.6.2 details</summary> ## [1.6.2] - 2025-08-15 ### Changed - **CLI Architecture**: Refactored CLI entry point from `__init__.py` to dedicated CLI module - Moved CLI functionality to `stata_mcp/cli/_cli.py` - Improved code modularity and separation of concerns - Updated entry point configuration in `pyproject.toml` - Enhanced maintainability following Python packaging best practices ### Technical - **Code Organization**: Clean separation between package initialization and CLI execution - **Entry Point**: Updated to use dedicated CLI module instead of `__init__.py` - **Module Structure**: Reduced complexity in main module initialization </details> <details> <summary>Click to expand v1.6.1 details</summary> ## [1.6.1] - 2025-08-09 ### Fixed - Fixed Excel file reading issue in `get_data_info` function by adding missing `openpyxl` dependency - Resolved compatibility issues with Excel (.xlsx) file formats ### Added - Added `openpyxl>=3.1.5` to project dependencies for Excel file support ### Security - Updated license from MIT to Apache License 2.0 for better legal protection and compatibility ### Changed - **License**: Migrated from MIT License to Apache License 2.0 - Updated LICENSE file to Apache 2.0 full text - Updated all documentation files (README, README-cn, README-fr, README-sp) - Updated CITATION.cff license field - Updated pyproject.toml license field - Updated Statement.md in all languages (中文, English, Français) - Updated all license badges from MIT to Apache 2.0 - Ensured consistent Apache 2.0 licensing across entire project </details> <details> <summary>Click to expand v1.6.0 details</summary> ## [1.6.0] - 2025-06-28 ### Added - Initial release with core Stata-MCP functionality - Support for regression analysis via LLM integration - Multi-language documentation (English, Chinese, French, Spanish) - PyPI package distribution - Jupyter integration support - Web UI interface - Cross-platform support (macOS, Windows, Linux) ### Features - Stata command execution via MCP protocol - Data analysis automation - Regression model building assistance - Statistical output interpretation - Code generation and debugging support </details>