LoL Data MCP Server

# LoL Data MCP Server - Complete Project Documentation **Version:** 2.3.1 (Performance Optimized) **Date:** July 2025 **Project Goal:** To develop a comprehensive Model Context Protocol (MCP) server that provides real-time, structured access to League of Legends game data, advanced gameplay analysis, **prepared training datasets for imitation learning**, and AI research capabilities for development environments, AI agents, and reinforcement learning applications. --- **🏗️ Updated Project Structure** ``` Project Taric/Lol_Data_MCP_Server/ ├── src/ │ ├── mcp_server/ │ │ ├── __init__.py │ │ ├── stdio_server.py # ✅ Stdio MCP server for Cursor │ │ ├── server.py # FastAPI web server (if needed) │ │ ├── mcp_handler.py # ✅ UPDATED: Handles 7 tools │ │ └── tools.py # ✅ 7 LoL data tools implemented │ ├── services/ │ │ ├── champion_service.py # ✅ Live wiki data with fallback │ │ ├── stats_service.py # ✅ Level-specific stats service │ │ ├── abilities_service.py # ✅ Enhanced ability details service │ │ └── patch_note_service.py # ✅ Champion patch history service │ ├── data_sources/ │ │ └── scrapers/ │ │ ├── base_scraper.py # ✅ Enhanced base class with caching │ │ ├── wiki_scraper.py # ✅ General wiki scraping │ │ ├── stats_scraper.py # ✅ Selenium level-specific scraping │ │ ├── abilities_scraper.py # ✅ Detailed ability mechanics │ │ ├── patch_note_scraper.py # ✅ Patch history extraction │ │ └── items/ # ✅ COMPLETED: Item scraper modules │ │ ├── __init__.py # ✅ Package initialization │ │ └── item_data_scraper.py # ✅ Comprehensive item data scraper │ └── core/ │ └── config.py # ✅ Multi-environment config system ├── config/ # ✅ YAML configuration files ├── docs/ # ✅ Comprehensive documentation ├── tests/ # ✅ Extensive test suite └── venv/ # ✅ Virtual environment ``` **🎮 Core MCP Tools (9 Available)** 1. **get_champion_stats** - Retrieves champion statistics with optional level parameter (1-18) 2. **get_champion_abilities** - Comprehensive ability details with enhanced mechanics 3. **get_ability_details** - Enhanced ability details with Details tab interaction 4. **get_champion_patch_note** - Historical patch changes and balance updates 5. **search_champions** - Champion search with fuzzy matching capabilities 6. **get_item_data** - ✅ **COMPLETED**: Item statistics, recipe, cost analysis, notes, similar items 7. **get_item_patch_note** - ✅ **COMPLETED**: Item patch history and balance changes 8. **get_rune_data** - ✅ **COMPLETED**: Rune sidebar, notes, and strategy information 9. **get_rune_patch_note** - ✅ **COMPLETED**: Rune patch history and balance changes 10. **ping** - Connectivity testing and health check 11. **server_info** - Server status and comprehensive information **🔧 Cursor Integration Configuration** ```json { "mcpServers": { "lol-data": { "command": "powershell", "args": ["-Command", "& { cd 'C:\\Users\\tarik\\OneDrive\\Masaüstü\\Python\\Reinforcement Learning Projects\\Project Taric\\Lol_Data_MCP_Server'; .\\venv\\Scripts\\Activate.ps1; python -m src.mcp_server.stdio_server }"] } } } ``` ## I. Vision & Scope ### Problem Statement - **Data Fragmentation**: LoL data is scattered across wiki, APIs, and community sources - **Development Friction**: Manual data gathering slows development of LoL-related projects - **Data Staleness**: Game patches constantly change champion/item stats - **Format Inconsistency**: Different data sources use different formats and structures - **Training Data Gaps**: No unified system for generating AI training data from gameplay - **Analysis Complexity**: Sophisticated gameplay analysis requires complex data processing - **Imitation Learning Barriers**: Researchers need processed, ready-to-train datasets, not raw data ### Solution Create a unified, intelligent MCP server that: - **Aggregates** data from multiple sources (Wiki, Riot API, Community APIs) - **Normalizes** data into consistent, structured formats - **Caches** intelligently for performance while staying current - **Serves** data through MCP protocol for direct IDE integration - **Generates** state-action pairs for reinforcement learning - **Analyzes** gameplay frame-by-frame with enhanced metrics - **Provides** **ready-to-train datasets** for imitation learning and AI research - **Delivers** both raw and processed data based on user needs ### Success Criteria - **Performance**: Sub-100ms response times for cached data - **Accuracy**: 99.9% data accuracy compared to official sources - **Coverage**: 160+ champions, 200+ items, all major game mechanics - **Integration**: Seamless MCP integration with popular IDEs - **Reliability**: 99.5% uptime with automatic failover - **Training Data**: Generate comprehensive state-action pairs for any champion - **Analysis Depth**: Frame-by-frame analysis with 40+ gameplay scenarios - **Imitation Learning Ready**: Serve processed datasets immediately usable for training --- ## II. Requirements & Features ### R1: MCP Protocol Implementation - **R1.1**: Full MCP server compliance with protocol specification - **R1.2**: WebSocket and HTTP transport support - **R1.3**: Standard MCP tool definitions for all data endpoints - **R1.4**: Error handling and status reporting via MCP - **R1.5**: Client authentication and rate limiting ### R2: Data Sources Integration - **R2.1**: League of Legends Wiki scraping and parsing - **R2.2**: Riot Games API integration (Data Dragon, Live API) - **R2.3**: Community API integration (op.gg, lolalytics, etc.) - **R2.4**: Patch detection and automatic data refresh - **R2.5**: Data validation and conflict resolution ### R3: Champion Data Management - **R3.1**: Complete champion roster (160+ champions) - **R3.2**: Base stats and growth per level - **R3.3**: Ability details (damage, cooldowns, scaling, mechanics) - **R3.4**: Recommended builds and skill orders - **R3.5**: Historical patch changes and meta evolution ### R4: Item Data Management - **R4.1**: All items with stats and costs - **R4.2**: Item components and build paths - **R4.3**: Active and passive effects - **R4.4**: Item interactions and synergies - **R4.5**: Cost efficiency calculations ### R5: Game Mechanics Data - **R5.1**: Minion stats and wave patterns - **R5.2**: Turret behavior and stats - **R5.3**: Jungle monsters and objectives - **R5.4**: Experience and gold curves - **R5.5**: Game rules and formulas ### R6: Query Engine & Search - **R6.1**: Complex queries across data relationships - **R6.2**: Full-text search capabilities - **R6.3**: Filtering and sorting options - **R6.4**: Aggregation and statistics - **R6.5**: Query optimization and caching ### R7: Performance & Reliability - **R7.1**: Intelligent caching with TTL management - **R7.2**: Database optimization for fast queries - **R7.3**: Horizontal scaling capabilities - **R7.4**: Health monitoring and alerting - **R7.5**: Graceful degradation under load ### 🚀 R8: Advanced Gameplay Analysis - **R8.1**: Frame-by-frame match timeline analysis - **R8.2**: State-action pair generation for reinforcement learning - **R8.3**: Enhanced data extraction (positioning, combat metrics, decision context) - **R8.4**: Player-specific gameplay pattern analysis - **R8.5**: Comprehensive scenario generation (40+ gameplay scenarios) ### 🎯 R9: Imitation Learning Data Services (NEW) - **R9.1**: **Ready-to-train dataset generation** for immediate use - **R9.2**: **Preprocessed state-action pairs** with enhanced features - **R9.3**: **Multi-format export** (JSON, Parquet, HDF5, PyTorch tensors) - **R9.4**: **Player-specific demonstration datasets** from high-ELO players - **R9.5**: **Scenario-labeled training data** for targeted learning - **R9.6**: **Data quality validation** and filtering for training suitability - **R9.7**: **Incremental dataset updates** and versioning ### 📊 R10: Training Data Generation - **R10.1**: High-ELO player data collection and processing - **R10.2**: Champion-specific training dataset creation - **R10.3**: Temporal gameplay event extraction - **R10.4**: Cooldown tracking with ability haste calculations - **R10.5**: Multi-level reward signal computation ### 📈 R11: Personalized Analytics - **R11.1**: Individual player performance analysis - **R11.2**: Meta comparison and trend analysis - **R11.3**: Build effectiveness scoring against current meta - **R11.4**: Gameplay improvement recommendations - **R11.5**: Cross-patch performance tracking --- ## III. Detailed Implementation Plan (Task-Oriented for AI Assistant) This section breaks down the Requirements (R-sections) into granular, sequential tasks for implementation. Each task specifies its objective, target file(s), and precise instructions. **Overall Approach:** Iterative development starting with a minimal viable MCP server, then progressively adding data sources, caching, advanced analysis features, and **imitation learning dataset generation**. **Priority: Get researchers training-ready data quickly while building comprehensive analysis capabilities.** --- ## Phase 1: MCP Server Foundation (COMPLETED ✅) ### ✅ **Task 1.1: Project Structure and Environment Setup** *(COMPLETED)* **Objective:** Create comprehensive project structure with proper Python package layout **File(s):** `pyproject.toml`, `requirements.txt`, basic folder structure **Status:** ✅ **COMPLETED** - December 2024 **Instructions for AI:** 1. ✅ Create root directory `Lol_Data_MCP_Server/` 2. ✅ Initialize Git repository 3. ✅ Create `pyproject.toml` with project metadata and dependencies (FastAPI, pydantic, etc.) 4. ✅ Create `requirements.txt` with core and development dependencies. 5. ✅ Create basic folder structure: `src/`, `tests/`, `config/`, `docs/`, `examples/`. 6. ✅ Set up `src/` as a Python package with proper `__init__.py` files. 7. ✅ Create a comprehensive `.gitignore` file for Python projects. 8. ✅ Implement a `setup_environment.py` script for automated environment checks. **Verification:** ✅ Project can be installed with `pip install -e .` and the environment can be checked with `python setup_environment.py`. - **VERIFIED** **✅ What Was Accomplished:** - ✅ Created a complete directory structure including `src/`, `tests/`, `config/`, `docs/`, and `examples/`. - ✅ Set up proper Python packaging with `__init__.py` files in all necessary directories. - ✅ Configured the virtual environment with `requirements.txt`, containing over 35 dependencies. - ✅ Implemented `setup_environment.py` script for automated environment validation. - ✅ Created a comprehensive `.gitignore` file tailored for Python projects. - ✅ Established `pyproject.toml` with project metadata and build configuration. **📁 Files Created:** - `src/` package structure with 8 subdirectories. - `config/` with 5 YAML configuration files. - `tests/` with 6 test modules. - `examples/` with client examples and integration demos. - Root-level configuration files (`requirements.txt`, `pyproject.toml`, `.gitignore`). - Environment setup script `setup_environment.py`. **🔧 Key Achievements:** - A professional project structure following Python best practices. - An automated environment setup script (`setup_environment.py`) to ensure consistency. - Virtual environment isolation for robust dependency management. - A comprehensive configuration system supporting multiple environments. --- ### ✅ **Task 1.2: Basic MCP Server Implementation** *(COMPLETED)* **Objective:** Implement core MCP protocol server with FastAPI and WebSocket support **File(s):** `src/mcp_server/server.py`, `src/mcp_server/mcp_handler.py` **Status:** ✅ **COMPLETED** - All requirements implemented and tested **Instructions for AI:** 1. ✅ Create `MCPServer` class using FastAPI 2. ✅ Implement WebSocket endpoint for MCP protocol at `/mcp` 3. ✅ Add basic MCP message handling (initialize, list_tools, call_tool) 4. ✅ Create health check endpoint `/health` for monitoring 5. ✅ Add basic error handling and logging with structured logs 6. ✅ Implement graceful shutdown with proper resource cleanup 7. ✅ Add lifespan management for application startup/shutdown 8. ✅ Create comprehensive test suite for MCP functionality **Verification:** ✅ Server starts and responds to MCP `initialize` and `list_tools` requests - **VERIFIED** **✅ What Was Accomplished:** - ✅ Created FastAPI application with lifespan management for proper startup/shutdown - ✅ Implemented WebSocket endpoint at `/mcp` for MCP protocol communication - ✅ Added health check endpoint at `/health` for monitoring - ✅ Built MCPHandler class for processing MCP protocol messages - ✅ Implemented core MCP methods: initialize, list_tools, call_tool - ✅ Added proper JSON-RPC 2.0 message handling with error responses - ✅ Configured structured logging with contextual information **📁 Files Created/Updated:** - `src/mcp_server/server.py` - Main FastAPI server with WebSocket support - `src/mcp_server/mcp_handler.py` - Core MCP protocol message handling - Signal handling for graceful shutdown - Comprehensive error handling and logging **🔧 Key Achievements:** - Full MCP protocol compliance with proper message format - Asynchronous WebSocket communication for real-time data access - Graceful startup/shutdown with resource cleanup - Health monitoring for production deployment readiness - Structured logging for debugging and monitoring --- ### ✅ **Task 1.3: Tool Registry and Validation System** *(COMPLETED)* **Objective:** Create robust tool management system with validation and error handling **File(s):** `src/mcp_server/tools.py` **Status:** ✅ **COMPLETED** **Instructions for AI:** 1. ✅ Create `MCPTool` abstract base class with schema validation. 2. ✅ Define `GetChampionDataTool` with its corresponding Pydantic input model for validation. 3. ✅ Define `GetAbilityDetailsTool` with its Pydantic input model for validation. 4. ✅ Create a `ToolRegistry` class for dynamic, programmatic loading and management of tools. 5. ✅ Add comprehensive input parameter validation using Pydantic models within each tool's implementation. 6. ✅ Implement robust error handling for tool execution failures. **Verification:** ✅ Core tools (`get_champion_data`, `get_ability_details`) validate inputs and return structured outputs. - **VERIFIED** **✅ What Was Accomplished:** - ✅ Implemented a `ToolRegistry` class for centralized, programmatic tool management. - ✅ Created an `MCPTool` abstract base class to enforce a consistent structure. - ✅ Built a tool registration system where tools are defined in code, not from external configs. - ✅ Added input parameter validation using Pydantic models for type safety. - ✅ Implemented comprehensive error handling for tool execution within the `MCPHandler`. - ✅ Created tool discovery and listing functionality based on registered tool instances. **📁 Files Created/Updated:** - `src/mcp_server/tools.py` - Contains the complete tool registry, base classes, and concrete tool implementations (`GetChampionDataTool`, `GetAbilityDetailsTool`). - Pydantic input models are co-located with the tools for validation. - The `ToolRegistry` provides an async execution framework. **🔧 Key Achievements:** - Type-safe tool parameter validation using Pydantic. - An extensible tool architecture that allows for easy addition of new tools in code. - Comprehensive error handling with proper MCP error codes managed by the `MCPHandler`. - Schema-driven tool definitions that ensure consistency. - Automated tool discovery and registration at server startup. --- ### ✅ **Task 1.4: Champion Service Implementation** *(COMPLETED)* **Objective:** Create a comprehensive champion data service with live WikiScraper integration and intelligent mock data fallback. **File(s):** `src/services/champion_service.py` **Status:** ✅ **COMPLETED** **Instructions for AI:** 1. ✅ Create a `ChampionService` class with Pydantic models for structured data (ChampionData, ChampionStats, etc.). 2. ✅ Integrate the `WikiScraper` to fetch live champion data from the LoL Wiki. 3. ✅ Implement data transformation methods (`_transform_wiki_stats`, `_transform_wiki_abilities`) to convert raw scraped data into validated Pydantic models. 4. ✅ Implement intelligent fallback logic: attempt to fetch from the wiki first, and only use internal mock data if the wiki call fails or returns incomplete data. 5. ✅ Add comprehensive error handling for network issues, parsing failures, and missing champions. 6. ✅ Implement champion name normalization to handle inconsistencies. 7. ✅ Retain mock data for "Taric" as a reliable fallback for testing and development. 8. ✅ Connect the service to the MCP tool system, allowing tools to serve live data. **Verification:** ✅ MCP tool `get_champion_data` returns real, valid data for any champion (e.g., "Samira", "Akali") and falls back to mock data for "Taric" if the wiki is unavailable. - **VERIFIED** **✅ What Was Accomplished:** - ✅ Created a `ChampionService` class that now serves as a live data provider, not just a mock service. - ✅ Fully integrated the `WikiScraper` to fetch real-time champion stats and abilities. - ✅ Implemented robust Pydantic models for type-safe data validation and serialization. - ✅ Built a data transformation layer to map raw scraped HTML into structured, clean data. - ✅ Developed an intelligent fallback system that prioritizes live data but gracefully degrades to mock data on failure. - ✅ Added comprehensive error handling and logging for a resilient service. - ✅ Kept "Taric" mock data to ensure core functionality is always testable. **📁 Files Created/Updated:** - `src/services/champion_service.py` - A complete, production-ready champion service that connects to live data sources. - Pydantic models for comprehensive data validation. - Transformation logic to process and clean external data. - Fallback mechanisms for high reliability. **🔧 Key Achievements:** - Transitioned from a static mock data service to a dynamic, live data service. - Type-safe champion data with comprehensive validation for any champion. - Highly resilient and extensible service architecture that can incorporate more data sources in the future. - Robust error handling and logging for production readiness. - High test coverage ensuring reliability of both live and fallback data paths. --- ### ✅ **Task 1.5: Configuration Management System** *(COMPLETED)* **Objective:** Implement comprehensive configuration management supporting multiple environments **File(s):** `src/core/config.py`, `config/server_config.yaml`, `config/development_config.yaml`, `config/production_config.yaml` **Status:** ✅ **COMPLETED** **Instructions for AI:** 1. ✅ Create a `Settings` class using Pydantic's `BaseSettings` with nested configuration models (`ServerConfig`, `DatabaseConfig`, etc.). 2. ✅ Implement support for loading configurations from environment variables (e.g., from a `.env` file) with type validation. 3. ✅ Add a sophisticated loading mechanism that reads from multiple YAML files (`server_config.yaml`, `data_sources.yaml`, and environment-specific files like `development_config.yaml`). 4. ✅ Implement environment variable substitution within YAML files (e.g., `${API_KEY}` can be replaced by an environment variable). 5. ✅ Create development and production environment settings with appropriate, secure defaults. 6. ✅ Implement comprehensive config validation using Pydantic validators to ensure data integrity. 7. ✅ Add helper methods for common configuration access patterns (e.g., `is_development()`, `get_database_url()`). **Verification:** ✅ Server correctly loads configuration from YAML files and environment variables, with environment-specific overrides working as expected. - **VERIFIED** **✅ What Was Accomplished:** - ✅ Implemented a comprehensive `Settings` class with nested configuration sections for clarity and structure. - ✅ Added support for loading configurations from both environment variables and a hierarchy of YAML files. - ✅ Created a custom `EnvironmentLoader` to allow for dynamic environment variable substitution inside YAML files. - ✅ Built a robust system for environment-specific overrides (`development_config.yaml`, `production_config.yaml`). - ✅ Added comprehensive validation with helpful error messages using Pydantic's validation features. - ✅ Implemented fallback behavior to ensure the application can start even with missing configuration files. **📁 Files Created/Updated:** - `src/core/config.py` - The main configuration management system with the `Settings` class. - `src/core/environment_loader.py` - A custom loader for YAML files with environment variable support. - `config/development_config.yaml` - Overrides for the development environment. - `config/production_config.yaml` - Secure overrides for the production environment. - `tests/test_config.py` - A comprehensive test suite to ensure configuration loading is reliable. **🔧 Key Achievements:** - A highly flexible, multi-environment configuration system. - Type-safe configuration management with Pydantic models. - Secure and dynamic configuration loading from files and environment variables, preventing hardcoded secrets. - Comprehensive test coverage ensuring the reliability of the configuration system. - A production-ready configuration management system that is easy to extend. --- ### ✅ **Task 1.6: Cursor MCP Integration** *(COMPLETED)* **Objective:** Integrate MCP server with Cursor IDE for seamless development workflow **File(s):** `src/mcp_server/stdio_server.py`, Cursor configuration **Status:** ✅ **COMPLETED** - Successfully integrated with Cursor IDE **Instructions for AI:** 1. ✅ Create stdio-based MCP server for Cursor integration 2. ✅ Implement PowerShell-compatible server startup script 3. ✅ Configure Cursor MCP settings for Turkish character path handling 4. ✅ Test all 7 MCP tools (5 LoL data tools + 2 basic tools) 5. ✅ Verify champion data access (Taric and Ezreal) 6. ✅ Ensure proper error handling and logging 7. ✅ Document configuration steps and troubleshooting **Verification:** ✅ All MCP tools accessible and functional within Cursor IDE - **VERIFIED** **✅ What Was Accomplished:** - ✅ Created stdio_server.py for direct Cursor IDE integration - ✅ Configured PowerShell-based startup handling Turkish character paths - ✅ Successfully registered 7 MCP tools with Cursor - ✅ Verified functionality of all core tools (get_champion_data, search_champions, etc.) - ✅ Integrated comprehensive champion data (Taric and Ezreal) - ✅ Implemented proper error handling and status reporting **📁 Files Created/Updated:** - `src/mcp_server/stdio_server.py` - Stdio MCP server for Cursor - Cursor MCP configuration with PowerShell support - Integration documentation and troubleshooting guides **🔧 Key Achievements:** - Seamless Cursor IDE integration with 7 working MCP tools - PowerShell-compatible configuration handling special characters - Real-time champion data access within development environment - Production-ready MCP server architecture - Complete development workflow integration --- ### ✅ **Task 1.7: PowerShell Unicode Path Fix** *(COMPLETED)* **Objective:** Fix PowerShell terminal crashes caused by Turkish characters in file path **File(s):** `CURSOR_INTEGRATION.md`, updated MCP configurations **Status:** ✅ **COMPLETED** - Fixed configurations provided and documented **Instructions for AI:** 1. ✅ Identify PowerShell Unicode handling issue with Turkish characters (`Masaüstü`) 2. ✅ Create Command Prompt alternative configuration for Cursor MCP 3. ✅ Develop PowerShell UTF-8 encoding solution as alternative 4. ✅ Update CURSOR_INTEGRATION.md with fixed configurations 5. ✅ Document troubleshooting steps and user actions required 6. ✅ Test configurations to ensure MCP server functionality 7. ✅ Update technical documentation with complete fix details **Verification:** ✅ Fixed configurations eliminate PowerShell crashes and maintain MCP functionality - **VERIFIED** **✅ What Was Accomplished:** - ✅ Diagnosed PowerShell PSReadLine Unicode issue with Turkish characters - ✅ Created Command Prompt (cmd) based MCP configuration as primary solution - ✅ Developed PowerShell UTF-8 encoding alternative for users preferring PowerShell - ✅ Updated CURSOR_INTEGRATION.md with comprehensive fix documentation - ✅ Provided clear user action steps for configuration updates - ✅ Maintained full MCP server functionality with all 7 tools working - ✅ Committed fix to GitHub repository with proper documentation **📁 Files Created/Updated:** - `CURSOR_INTEGRATION.md` - Complete fix documentation with multiple configuration options - `docs/lol_data_mcp_server.md` - Technical documentation update with fix details - `README.md` - Current task status update **🔧 Key Achievements:** - Resolved critical Unicode path issue preventing MCP server use - Provided multiple configuration solutions for different user preferences - Maintained backwards compatibility while fixing the core issue - Comprehensive documentation for troubleshooting and future reference - Successfully tested and verified fix effectiveness --- ### ✅ **Task 1.8: Client Examples Review & Optimization** *(COMPLETED)* **Objective:** Consolidate and rewrite client examples into a single, functional, and comprehensive integration demonstration. **File(s):** `examples/project_integration.py` **Status:** ✅ **COMPLETED** **Instructions for AI:** 1. ✅ Audit all existing files in the `examples/` directory. 2. ✅ Remove any broken, empty, or outdated example scripts. 3. ✅ Consolidate multiple example concepts into a single, well-structured file: `examples/project_integration.py`. 4. ✅ Implement a standalone demonstration script that can run independently and showcases key use cases. 5. ✅ Add a mock service fallback within the script so it can run even without a live server connection. 6. ✅ Create three distinct demo functions: one for ML training integration, one for comparative analysis, and one for game simulation setup. 7. ✅ Ensure the script generates example output files (`taric_training_data.json`, `simulation_config.json`) to serve as clear artifacts. **Verification:** ✅ The `examples/project_integration.py` script runs successfully and demonstrates multiple real-world integration patterns. - **VERIFIED** **✅ What Was Accomplished:** - ✅ Replaced a messy structure of outdated examples with a single, powerful demonstration script: `project_integration.py`. - ✅ Created a comprehensive example that showcases three major use cases: ML training, data analysis, and game simulation. - ✅ Implemented a graceful fallback using a mock `ChampionService` to ensure the example is always runnable. - ✅ Generated practical, structured output files (`.json`) that can be used as templates for other projects. - ✅ Provided a clear, functional example of how to integrate the `ChampionService` into a larger application. **📁 File Changes:** - `examples/project_integration.py`: A new, comprehensive script demonstrating multiple integration patterns. - `examples/taric_training_data.json`: Example output for an ML training pipeline. - `examples/simulation_config.json`: Example output for configuring a game simulation. - Removed: All old, broken, or empty example files and subdirectories. **💡 Key Benefits:** - A working, high-quality example that clearly demonstrates the value and usage of the `ChampionService`. - Practical integration patterns that can be copied and adapted for real projects. - A more professional and streamlined `examples` directory. --- ## Phase 2: Data Sources Integration ### Task 2.1: Implement LoL Wiki Scraper ### ✅ **Task 2.1.1: Create Basic Wiki Scraper Foundation** *(COMPLETED)* **Objective:** Set up the basic scraper infrastructure and HTTP handling **Files:** `src/data_sources/scrapers/wiki_scraper.py`, `src/data_sources/scrapers/__init__.py` **Status:** ✅ **COMPLETED** **Instructions:** 1. ✅ Create `scrapers/` folder in `src/data_sources/` 2. ✅ Create `WikiScraper` class using httpx and BeautifulSoup 3. ✅ Implement basic HTTP client with user-agent headers 4. ✅ Add rate limiting (1 request per second) using asyncio 5. ✅ Implement basic URL handling for LoL Wiki champion pages 6. ✅ Add connection timeout and retry configuration 7. ✅ Create basic logging for scraper operations **Verification:** ✅ Can successfully fetch a champion page HTML (e.g., Taric) with proper rate limiting **✅ What Was Accomplished:** - ✅ Created complete WikiScraper class with async HTTP handling - ✅ Implemented rate limiting (1 req/sec), retry logic with exponential backoff - ✅ Added comprehensive error handling (404s, timeouts, connection errors) - ✅ Built champion URL handling with special character support - ✅ Created professional user agent and proper HTTP headers - ✅ Implemented async context manager for resource management - ✅ Added comprehensive logging at all levels - ✅ Created 6 unit tests with 100% pass rate - ✅ Verified real LoL Wiki connectivity and champion page fetching **📁 Files Created:** - `src/data_sources/scrapers/wiki_scraper.py` - Main scraper implementation (1300+ lines) - `src/data_sources/scrapers/__init__.py` - Package initialization - `tests/test_wiki_scraper.py` - Comprehensive test suite **🔧 Key Features:** - Async HTTP client with httpx and BeautifulSoup - Rate limiting and retry logic with exponential backoff - Champion URL building with special character handling (Kai'Sa, Twisted Fate) - Comprehensive error handling and logging - Professional user agent for responsible scraping - Connection testing and health checks #### ✅ **Task 2.1.2: Implement Champion Page Navigation** *(COMPLETED)* **Objective:** Navigate wiki pages and identify data sections **Files:** `src/data_sources/scrapers/wiki_scraper.py` **Status:** ✅ **COMPLETED** - December 2024 **Instructions:** 1. ✅ Add `find_champion_data_sections()` method 2. ✅ Implement CSS selectors for champion info tables 3. ✅ Add navigation to different wiki page sections (stats, abilities) 4. ✅ Create page structure validation 5. ✅ Handle different wiki page layouts and formats 6. ✅ Add error handling for missing sections **Verification:** ✅ Can navigate to and identify stats/abilities sections on champion pages **✅ What Was Accomplished:** - ✅ Implemented `find_champion_data_sections()` method that identifies 3 key sections: stats, abilities, overview - ✅ Created `_find_stats_section()` with multiple fallback strategies: - Primary: CSS selector for `infobox type-champion-stats` class - Fallback 1: Content-based detection using stat abbreviations (HP, MP, AD, AR, MR) - Fallback 2: Table-based detection for "base statistics" content - ✅ Implemented `_find_abilities_section()` with comprehensive navigation: - Primary: MediaWiki headline span detection for "Abilities" sections - Fallback 1: Header-based detection with content extraction - Fallback 2: Class-based detection for ability-related divs - ✅ Added `_find_overview_section()` for champion description detection - ✅ Created `_validate_page_structure()` method for comprehensive validation - ✅ Implemented proper error handling and logging for missing sections - ✅ Added comprehensive type safety with BeautifulSoup Tag validation **📊 Implementation Details:** - **Stats Detection**: Successfully identifies champion stats infoboxes on real LoL Wiki pages - **Abilities Detection**: Navigates MediaWiki section structure to extract abilities content - **Multiple Fallbacks**: Robust detection even with different page layouts - **Type Safety**: Proper isinstance() checks for BeautifulSoup elements - **Error Handling**: Graceful degradation when sections are missing **🧪 Testing Results:** - ✅ Successfully tested with Taric and Ezreal champion pages - ✅ Stats section: 100% detection rate with HP, AD, MP, AR, MR data - ✅ Abilities section: Working detection with content extraction - ✅ Page validation: Comprehensive validation logic implemented - ✅ Integration: Added 6 new unit tests to test suite **📁 Files Modified:** - `src/data_sources/scrapers/wiki_scraper.py`: Added 150+ lines of navigation methods - `tests/test_wiki_scraper.py`: Added comprehensive test coverage for Task 2.1.2 **🎯 Ready for Task 2.1.3:** Champion stats table parsing implementation #### ✅ **Task 2.1.3: Parse Champion Stats Table** *(VERIFIED & FIXED)* **Objective:** Extract numerical stats from champion info tables **Files:** `src/data_sources/scrapers/wiki_scraper.py` **Status:** ✅ **VERIFIED & FIXED** - December 2024 (Critical bug fixed) **Instructions:** 1. ✅ Implement `parse_champion_stats()` method 2. ✅ Extract base stats (HP, Mana, AD, Armor, MR, etc.) 3. ✅ Parse per-level growth stats (**CRITICAL FIX APPLIED**) 4. ✅ Handle different stat table formats 5. ✅ Add data validation and type conversion 6. ✅ Create fallback parsing for edge cases 7. ✅ Add unit handling (flat vs percentage values) **Verification:** ✅ **VERIFIED WITH REAL WIKI DATA** - Tested with live Taric page, all stats extracted correctly **🚨 CRITICAL BUG FOUND & FIXED:** - **Problem**: Original regex pattern expected "HP: 645 (+99)" format, but real LoL wiki uses "HP645+99" format - **Impact**: Growth values were never extracted, making level-based calculations impossible - **Fix**: Updated `_extract_stat_value()` method with new regex patterns for real wiki format - **Result**: All base and growth values now extracted correctly from live wiki pages **✅ What Was Accomplished:** - ✅ **FIXED** `_extract_stat_value()` method with new regex patterns: - **Pattern 1**: "HP645+99" (actual LoL wiki format) 🔥 **NEW** - **Pattern 2**: "HP: 645 (+99)" (legacy fallback format) - **Pattern 3**: "HP645" (no growth format) - ✅ **VERIFIED** with real Taric wiki data - all stats working: - HP: 645 + 99 per level ✅ - MP: 300 + 60 per level ✅ - AD: 55 + 3.5 per level ✅ - Armor: 40 + 4.3 per level ✅ - MR: 28 + 2.05 per level ✅ - ✅ **TESTED** level calculations: Taric Level 6 HP = 1140.0 (matches user requirement) - ✅ Implemented comprehensive stat name standardization - ✅ Added full validation ranges for all champion stats **🎯 Level-Based Stats Feature (User Requirement):** - ✅ **"Bring me level 6 stats for Taric"** - Working correctly! - ✅ Calculation: Base + (Growth × (Level - 1)) - ✅ Example: HP at Level 6 = 645 + (99 × 5) = 1140.0 - ✅ Supports all levels 1-18 for any champion with growth stats **🎮 Successfully Parsed Stats (Real Wiki Data):** - **HP (Health Points)**: 645 + 99 per level - **MP (Mana Points)**: 300 + 60 per level - **AD (Attack Damage)**: 55 + 3.5 per level - **Armor**: 40 + 4.3 per level - **MR (Magic Resistance)**: 28 + 2.05 per level - **Attack Speed**: 0.625 (no growth) - **Movement Speed**: 340 (no growth) - **Range**: 150 (no growth) **🔧 Technical Implementation:** - **Multiple Regex Patterns**: 3-tier pattern matching for maximum compatibility - **Real Wiki Format Support**: Handles "StatName[BaseValue]+[Growth]" format - **Fallback Support**: Legacy and edge case format handling - **Debug Logging**: Added detailed extraction logging for monitoring - **Type Safety**: Full type hints and validation maintained **🧪 Testing Results (Real Data):** - ✅ **Real Wiki Verification**: Successfully tested with live Taric wiki page - ✅ **Growth Value Extraction**: Fixed and working - 5/8 stats have growth values - ✅ **Level Calculations**: Verified level 1, 6, 11, 18 calculations work correctly - ✅ **Format Compatibility**: Handles real wiki format perfectly - ✅ **User Requirements**: Meets "level 6 stats" requirement completely **📁 Files Modified:** - `src/data_sources/scrapers/wiki_scraper.py`: **FIXED** `_extract_stat_value()` method - Added Pattern 1: `rf'{stat_name}\s*([0-9]+\.?[0-9]*)\s*\+\s*([0-9]+\.?[0-9]*)'` for "HP645+99" - Enhanced debug logging for successful extractions - Improved error handling for multiple pattern attempts **🎯 Ready for Task 2.1.4:** Champion abilities information parsing implementation #### ✅ **Task 2.1.4: Parse Champion Abilities Information** *(COMPLETED)* **Objective:** Extract ability details (Q, W, E, R, Passive) **Files:** `src/data_sources/scrapers/wiki_scraper.py` **Status:** ✅ **COMPLETED** - December 2024 **Instructions:** 1. ✅ Implement `parse_champion_abilities()` method 2. ✅ Extract ability names, descriptions, and cooldowns 3. ✅ Parse damage values and scaling information 4. ✅ Handle complex ability interactions and conditions 5. ✅ Add mana/energy cost extraction 6. ✅ Create ability range and effect area parsing 7. ✅ Handle champion-specific ability variations **Verification:** ✅ Can extract complete ability data for Taric (all 5 abilities with details) **✅ What Was Accomplished:** - ✅ Implemented comprehensive `parse_champion_abilities()` method with full champion ability extraction - ✅ Added 6 helper methods for robust ability parsing: - `_extract_single_ability()` - Extract individual ability data with multiple strategies - `_extract_ability_name()` - Extract ability names from headings and bold text - `_clean_ability_description()` - Clean and format ability descriptions - `_parse_ability_values()` - Parse cooldowns, costs, ranges, damage with regex - `_extract_ability_effects()` - Extract effects lists (healing, stunning, channeling) - `_validate_ability_data()` - Validate and clean parsed ability data - ✅ Created comprehensive ability parsing with multiple detection strategies: - **Strategy 1**: Heading-based detection (h3 tags with ability patterns) - **Strategy 2**: Class-based detection (divs with ability classes) - **Strategy 3**: Content-based detection (text containing ability keywords) - ✅ Implemented robust regex patterns for parsing: - **Cooldowns**: "Cooldown: 14/13/12/11/10" format - **Mana Costs**: "Mana Cost: 60/70/80/90/100" format - **Ranges**: "Range: 750" format - **Damage**: "80/120/160/200/240 (+0.4 AP)" format with scaling - **Healing**: "30/45/60/75/90 (+0.2 AP)" format with scaling - ✅ Added comprehensive effects detection with 15+ effect patterns: - Combat effects (stun, slow, knockup, silence, root) - Utility effects (heal, shield, dash, teleport, stealth) - Enhancement effects (increase stats, reduce cooldowns) - ✅ Created structured data output supporting all ability types: - **Passive abilities**: No cooldown/cost, damage scaling, effect descriptions - **Active abilities (Q/W/E/R)**: Full data with cooldowns, costs, ranges, effects - **Flexible fields**: Damage, healing, cost, range - all optional based on ability type **📊 Implementation Details:** - **Data Structure**: Comprehensive ability dictionary with 8 fields per ability - **Error Handling**: Graceful degradation when sections or data missing - **Type Safety**: Full type hints and validation throughout - **Logging**: Debug and info level logging for monitoring parsing progress - **Multiple Fallbacks**: 3-tier detection strategy ensures maximum compatibility - **Content Cleaning**: Smart description extraction avoiding stat lines **🧪 Testing Results:** - ✅ Successfully tested with mock Taric ability data - ✅ Parsed all 5 abilities: Passive (Bravado), Q (Starlight's Touch), W (Bastion), E (Dazzle), R (Cosmic Radiance) - ✅ Extracted cooldowns: Q (14/13/12/11/10), W (18/17/16/15/14), E (15/14/13/12/11), R (160/135/110) - ✅ Extracted mana costs: Q (60/70/80/90/100), W (60), E (60/65/70/75/80), R (100) - ✅ Extracted ranges: Q (750), W (800), E (575), R (400) - ✅ Extracted effects: Healing, channeling, shielding, stunning, invulnerability **🔧 CRITICAL BUG FIXES APPLIED:** - **CSS Selector Fix (December 2024)**: Fixed critical bug in `_extract_single_ability()` method where all abilities were returning same data - **Root Cause**: `find('div', class_=['skill', skill_class])` was always finding first container instead of specific ability containers - **Solution**: Changed to `find('div', class_=skill_class)` to properly match specific CSS classes (`skill_innate`, `skill_q`, `skill_w`, etc.) - **Result**: All 5 abilities now extract correctly with proper names (Bravado, Starlight's Touch, Bastion, Dazzle, Cosmic Radiance) - **Return Structure Fix**: Updated `parse_champion_abilities()` to return `{"abilities": {...}}` format expected by ChampionService - **CSS-Based Approach**: Replaced brittle text patterns with robust CSS selectors as recommended by Gemini code review - **Real Wiki Compatibility**: Confirmed working with actual LoL wiki HTML structure (`<div class="skill skill_q">`, etc.) **✅ Verification Results (Post-Fix):** - **Taric**: ✅ 5/5 abilities extracted (Passive=Bravado, Q=Starlight's Touch, W=Bastion, E=Dazzle, R=Cosmic Radiance) - **Yasuo**: ✅ 5/5 abilities extracted (Passive=Way of the Wanderer, Q=Steel Tempest, W=Wind Wall, E=Sweeping Blade, R=Last Breath) - **Real Descriptions**: ✅ Actual wiki content extracted (not mock data) - **Stats Parsing**: ✅ Cooldowns and costs extracted correctly (Q cooldown: 3, cost: 60 for Taric) **📁 Files Modified:** - `src/data_sources/scrapers/wiki_scraper.py`: Added 350+ lines of abilities parsing logic + CRITICAL BUG FIXES - Main method: `parse_champion_abilities()` (80 lines) + return structure fix - Helper methods: 6 comprehensive parsing and validation methods + 2 new CSS-based extraction methods - Regex patterns: 5 different value extraction patterns - Effects detection: 15+ effect pattern recognition - **NEW**: `_extract_ability_name_from_container()` - CSS-based name extraction with multiple strategies - **NEW**: `_extract_ability_description_from_container()` - CSS-based description extraction with JSON data support - `tests/test_wiki_scraper.py`: Added 3 new test methods for abilities parsing **🎯 Ready for Task 2.1.5:** Error handling and caching implementation #### ✅ **Task 2.1.5: Implement Error Handling and Caching** *(COMPLETED)* **Objective:** Add robust error handling and performance optimization **Files:** `src/data_sources/scrapers/wiki_scraper.py` **Status:** ✅ **COMPLETED** - December 2024 **Instructions:** 1. ✅ Implement comprehensive HTTP error handling 2. ✅ Add retry logic with exponential backoff (already existed, enhanced with metrics) 3. ✅ Create caching system for scraped pages (file-based) 4. ✅ Add cache expiration and invalidation 5. ✅ Implement graceful degradation on parsing errors 6. ✅ Add detailed logging and error reporting 7. ✅ Create scraping performance metrics **Verification:** ✅ Scraper handles network issues gracefully and caches data efficiently **✅ What Was Accomplished:** - ✅ **ScrapingMetrics dataclass** - Comprehensive performance tracking (requests, cache hits/misses, successes/failures, timing, errors) - ✅ **CacheManager class** - File-based caching with MD5 hash keys, 24-hour TTL, metadata management, cleanup functionality - ✅ **Enhanced WikiScraper** - Added caching support, metrics integration, safe parsing methods - ✅ **Graceful error handling** - Safe parsing methods (`parse_champion_stats_safe`, `parse_champion_abilities_safe`) return structured error info instead of crashing - ✅ **Performance metrics** - Request timing, cache statistics, error tracking with detailed logging - ✅ **Cache management** - TTL-based expiration, metadata storage, cleanup utilities (`cleanup_cache`, `get_cache_info`) **📁 Files Modified:** - `src/data_sources/scrapers/wiki_scraper.py`: Added 300+ lines of caching and metrics functionality - New imports: json, hashlib, datetime, pathlib, dataclasses - ScrapingMetrics dataclass with field factory for error list - CacheManager class with full file-based caching system - Enhanced WikiScraper with caching integration and metrics tracking - Safe parsing methods with graceful degradation - Cache utility methods for management and information **🔧 Key Features Added:** - **File-based caching**: `cache/wiki_pages/` directory with HTML content and JSON metadata - **Cache TTL**: 24-hour default expiration with configurable timeout - **Performance tracking**: Comprehensive metrics for requests, cache performance, parsing success/failure - **Error resilience**: Safe parsing methods that return partial data on failures - **Cache management**: Cleanup expired entries, cache statistics, hit/miss tracking - **Enhanced logging**: Detailed context and performance information throughout **🧪 Testing Results:** - ✅ All new functionality verified working correctly - ✅ Caching system creates cache directory and manages files properly - ✅ Metrics accurately track all operations (cache hits/misses, parsing failures, errors) - ✅ Safe parsing methods return structured error information instead of exceptions - ✅ Cache cleanup and information methods functioning correctly - ✅ No regression in existing functionality **🎯 Ready for Task 2.1.6:** WikiScraper-ChampionService Integration #### ✅ **Task 2.1.6: Connect WikiScraper to ChampionService** *(COMPLETED)* **Objective:** Replace mock data with real WikiScraper calls and add fallback logic **Files:** `src/services/champion_service.py` **Status:** ✅ **COMPLETED** - WikiScraper successfully integrated with intelligent fallback **Completed Implementation:** 1. ✅ **WikiScraper Integration:** Added async WikiScraper instance to ChampionService with proper initialization and cleanup 2. ✅ **Smart Fallback Logic:** Implemented intelligent fallback: WikiScraper → Mock Data (if wiki fails or data incomplete) 3. ✅ **Data Transformation:** Added robust transformation layer converting WikiScraper format to ChampionService format 4. ✅ **Champion Name Normalization:** Added `_normalize_champion_name()` for handling special characters and case sensitivity 5. ✅ **Comprehensive Error Handling:** Network timeouts, parsing failures, missing data - all handled gracefully 6. ✅ **Caching Integration:** WikiScraper's file-based caching (24-hour TTL) integrated at service level 7. ✅ **Honest Data Representation:** Shows None/unavailable for missing stats instead of fake defaults (user requested) **Technical Architecture:** - **Modified Data Models:** Made ChampionStats and ChampionAbilities fields Optional for honest data representation - **New Methods:** `_get_champion_from_wiki()`, `_transform_wiki_stats()`, `_transform_wiki_abilities()`, `_normalize_champion_name()` - **Enhanced Fallback:** Three data sources tracked: `wiki`, `mock_fallback_error`, `mock_fallback_incomplete` - **Configuration Options:** `enable_wiki=True/False` and `use_cache=True/False` for testing/production flexibility - **Resource Management:** Proper async cleanup of WikiScraper HTTP clients via `cleanup()` method **Data Source Intelligence:** - **Priority:** WikiScraper → Mock Data (fallback) - **Fallback Triggers:** Network errors, champion not found, incomplete data (no stats/abilities) - **Data Source Tracking:** Response includes `data_source` field indicating which source provided data - **Graceful Degradation:** Service always returns data, preferring real wiki data when available **Testing Coverage:** - ✅ **17 tests passing** including WikiScraper integration tests - ✅ **Mock data tests** (WikiScraper disabled for fast testing) - ✅ **Real wiki integration test** with proper error handling - ✅ **Fallback logic verification** with network simulation - ✅ **Data transformation testing** for stats and abilities **Performance & Reliability:** - **First Request:** ~2-3 seconds (network + parsing) - **Cached Requests:** <100ms (file cache hit) - **Fallback Time:** <500ms (immediate mock data) - **Success Rate:** 100% (always returns data via fallback) **User Experience:** - **Real Champion Data:** Any champion from LoL Wiki (Samira, Akali, Yasuo, etc.) - **Honest Representation:** Shows None for unavailable data instead of fake values - **Reliable Service:** Never fails - always provides data via intelligent fallback - **Fast Performance:** Caching ensures quick subsequent requests **Verification Results:** ✅ ChampionService successfully returns real wiki data for any champion (Samira, Akali, etc.) with proper fallback to mock data when needed #### ✅ **Task 2.1.7: Integrate WikiScraper with MCP Tools** *(COMPLETED)* **Objective:** Update MCP tools to use real wiki data through enhanced ChampionService **Files:** `src/mcp_server/tools.py`, `tests/test_tools.py` **Status:** ✅ **COMPLETED** - All MCP tools now use WikiScraper with real data **Implementation:** 1. ✅ Updated `GetChampionDataTool` - Already using WikiScraper-enabled ChampionService 2. ✅ Updated `GetAbilityDetailsTool` - Replaced 80+ lines of hardcoded data with WikiScraper integration 3. ✅ Added proper error handling for wiki scraping failures with graceful fallback to mock data 4. ✅ Implemented champion existence validation (built into ChampionService) 5. ✅ Added performance monitoring via WikiScraper caching and rate limiting 6. ✅ Tested MCP tools with real champion names (Taric working, Samira properly handled) 7. ✅ Enhanced MCP error responses with specific ChampionNotFoundError for invalid champions **Achieved Benefits:** - **All MCP tools** work with real LoL Wiki data with intelligent fallback - **Any champion** accessible via MCP (not limited to Taric/Ezreal hardcoded data) - **Real-time abilities** from official wiki with complete fallback system - **Improved error handling** with specific error messages for invalid champions - **Performance optimized** with built-in caching and rate limiting - **All ability types** working (passive, Q, W, E, R) **Verification Results:** ✅ All 15 tests pass, all ability types work, WikiScraper integration functional with proper fallback #### ✅ **Task 2.1.8: Implement Per-Level Stat Scraping** *(COMPLETED)* **Objective:** Fix incorrect stat calculations by scraping the complete per-level stats table directly from the wiki using level dropdown interaction. **Files:** `src/data_sources/scrapers/stats_scraper.py`, `src/services/stats_service.py`, `tests/test_stats_service.py` **Status:** ✅ **COMPLETED** - Per-level stat scraping with Selenium dropdown interaction + comprehensive tests **✅ Implementation:** 1. **✅ Complete StatsScraper:** Full implementation with Selenium level dropdown interaction - **Selenium Integration:** Uses `_create_selenium_driver()` from BaseScraper - **Level Dropdown:** Interacts with `#lvl_` selector to select specific levels (1-18) - **CSS Selectors:** ALL selectors from wiki_selectors.md implemented (15 total stats): - **Core Stats**: HP, Mana, AD, Armor, MR, AS, Movement Speed, Attack Range - **Advanced Stats**: Critical Damage, Base Attack Speed, Windup%, AS Ratio, HP/Mana Regen - **Timing Fix:** Proper `time.sleep(1.0)` wait for JavaScript stats update - **Error Handling:** Graceful handling of missing stats and Selenium failures 2. **✅ Enhanced StatsService:** Simplified service layer for level-specific stats - **Level-Specific Method:** `get_champion_stats(champion, level)` uses Selenium scraping - **Base Stats Fallback:** When no level specified, defaults to level 1 for consistency - **Data Source Tracking:** Returns `selenium_level_scrape` or `selenium_base_stats` - **Champion Name Normalization:** Handles special characters and spacing 3. **✅ MCP Tool Integration:** Existing `GetChampionStatsTool` already supports level parameter - **Optional Level:** Tool accepts `level` parameter (1-18) for accurate stats - **Service Integration:** Uses StatsService with dependency injection - **Error Responses:** Proper ChampionNotFoundError handling 4. **✅ Comprehensive Test Suite:** Complete test coverage for Task 2.1.8 implementation - **StatsService Tests:** Initialization, champion name normalization, level-specific scraping - **StatsScraper Tests:** CSS selectors validation, stat parsing, error handling - **Integration Tests:** End-to-end flow from service to scraper (mocked) - **Test Coverage:** 11/11 tests passing with proper mocking for non-Selenium parts **✅ Key Benefits:** - **100% Accurate Stats:** Scrapes actual displayed values instead of using formulas - **Level Dropdown Interaction:** Selenium automation of wiki level selection - **Fixes Stat Calculation Bug:** No more formula errors (Taric Level 13 HP = 1730 ✅) - **Simple & Clean Code:** Reduced from complex 350+ lines to focused 200 lines - **Real Wiki Data:** Uses live data from LoL Wiki with proper caching **✅ Technical Implementation:** - **Selenium WebDriver:** Chrome headless browser with proper driver management - **CSS Selectors:** All selectors from wiki_selectors.md for maximum accuracy - **JavaScript Wait:** Proper timing for wiki page stats updates - **Resource Management:** Proper driver cleanup and session management - **Caching Integration:** Inherits BaseScraper's 24-hour cache system **Verification:** ✅ The `get_champion_stats` tool with level parameter returns correct stat values matching in-game data for any champion at any level (1-18) #### ✅ **Task 2.1.9: Enhanced Champion Basic Stats** *(COMPLETED)* **Objective:** Extend champion stats to include detailed unit information and enhanced metrics for simulations **Files:** `src/data_sources/scrapers/stats_scraper.py` **Status:** ✅ **COMPLETED** - Unit radius data successfully extracted and integrated **✅ Implementation:** 1. **✅ Fixed Unit Radius Extraction:** Corrected CSS selectors and extraction logic - **Root Cause Identified:** Original CSS selectors were invalid - unit radius data is concatenated with labels in text - **HTML Structure Discovery:** Values appear as "Gameplay radius65", "Select. radius110" in page text - **Regex Pattern Solution:** Implemented pattern matching using `rf'{re.escape(label_text)}\s*(\d+)'` to extract values - **Updated UNIT_RADIUS_LABELS:** Replaced complex CSS selectors with simple label mapping 2. **✅ Enhanced StatsScraper:** Added working unit radius extraction to `_extract_unit_radius_data()` method - **Text-Based Extraction:** Searches page text for patterns like "Gameplay radius65" - **Robust Pattern Matching:** Uses regex to extract numeric values after radius labels - **Proper Integration:** Unit radius data included in `scrape_default_stat_ranges()` for base stats only - **Conditional Inclusion:** Only adds radius data when values exist (no N/A handling needed) 3. **✅ Base Stats vs Level Stats Differentiation:** Correctly implemented requirements - **Base Stats (No Level):** Includes unit radius data (`wiki_default_ranges` source) - **Level-Specific Stats:** Excludes unit radius data (`selenium_level_scrape` source) - **Service Integration:** Works seamlessly with existing StatsService architecture 4. **✅ Data Source Integration:** Full integration with MCP tools - **MCP Tool Support:** `get_champion_stats` tool now returns unit radius data for base stats - **No Breaking Changes:** Existing functionality preserved, only enhanced - **Caching Support:** Uses existing BaseScraper caching for performance **✅ Verification Results:** - **✅ Taric Base Stats:** Gameplay Radius (65), Selection Radius (135), Pathing Radius (35), Acquisition Radius (350) - **✅ Sona Base Stats:** Gameplay Radius (65), Selection Radius (110), Pathing Radius (35), Acquisition Radius (800) - **✅ Level-Specific Exclusion:** Taric Level 10 stats exclude unit radius data as required - **✅ Real Wiki Data:** Values match actual LoL Wiki displayed data perfectly - **✅ MCP Integration:** All unit radius data accessible via MCP tools **✅ Technical Achievement:** - **Fixed Critical Bug:** CSS selectors were completely wrong for this data type - **Innovative Solution:** Text pattern matching proved more reliable than complex CSS selectors - **Zero Regressions:** No impact on existing champion stats functionality - **Performance Optimized:** Uses existing HTTP-based scraping (no additional Selenium needed) **✅ Key Benefits Delivered:** - **Complete unit information** for simulation environments and AI positioning - **Collision detection data** for advanced gameplay algorithms - **Enhanced simulation support** with accurate unit dimensions - **Clean data model** - only shows data that exists, no empty fields **Verification:** ✅ Returns enhanced champion stats including unit radius data for base stats, excluded for level-specific stats, with values matching official LoL Wiki data --- #### ✅ **Task 2.1.10: Comprehensive Ability Detail System** *(COMPLETED)* **Objective:** Implement detailed ability information scraping using ability containers and CSS selectors **Files:** `src/data_sources/scrapers/abilities_scraper.py`, `src/services/abilities_service.py`, `src/models/data_models.py` **Status:** ✅ **COMPLETED** - Complete ability details with all game mechanics (December 2024) **✅ Implementation:** 1. **✅ AbilitiesScraper:** Complete scraper class inheriting from `BaseScraper` for ability-specific scraping - **CSS Selectors Implemented:** All ability containers from wiki_selectors.md: - **Passive Container**: `.skill_innate` - Main div for passive ability ✅ - **Q Ability Container**: `.skill_q` - Main div for Q ability ✅ - **W Ability Container**: `.skill_w` - Main div for W ability ✅ - **E Ability Container**: `.skill_e` - Main div for E ability ✅ - **R Ability Container**: `.skill_r` - Main div for R ability ✅ 2. **✅ Ability Details Extraction:** Within each container, extracts: - **Description**: `.ability-info-description` - Ability description text ✅ - **Stats List**: `.ability-info-stats__list` - Cost, cooldown, cast time, etc. ✅ 3. **✅ AbilitiesService:** Complete service layer for ability operations - **Data Transformation:** AbilitiesScraper data to model format ✅ - **Validation & Fallback:** Ability validation and fallback logic ✅ - **MCP Integration:** Clean interface for MCP tools ✅ 4. **✅ Enhanced Data Models:** Comprehensive ability data structure in `data_models.py` - **ChampionAbility Model:** `name`, `description`, `cooldown`, `cost`, `cast_time`, `range` ✅ - **All Ability Types:** Support for all ability types (Passive, Q, W, E, R) ✅ 5. **✅ Ability Mechanics Parsing:** Extracts detailed stats from ability containers - **Resource Costs:** Mana consumption vs fury generation ✅ - **Damage Values:** Scaling information (AP/AD ratios) ✅ - **Timing & Range:** Cast times, channel times, and ranges ✅ **✅ Technical Architecture:** - **Modular Design:** abilities_scraper + abilities_service architecture ✅ - **Consistent Pattern:** Following stats implementation pattern ✅ - **CSS Selector-Based:** Reliable data parsing using official selectors ✅ - **Dual-Form Support:** Handles complex champions (Nidalee, Jayce, etc.) ✅ - **Special Cases:** Handles unique champions (Aphelios weapon system) ✅ **✅ MCP Integration:** - **get_champion_abilities Tool:** Returns comprehensive ability data for all 5 abilities ✅ - **Ability-Specific Queries:** Supports ability_slot parameter for individual abilities ✅ - **Real Wiki Data:** Uses live data from LoL Wiki with "wiki_abilities_scrape" source ✅ **✅ Verification Results:** - **✅ Taric:** All 5 abilities (Passive=Bravado, Q=Touch, W=Bastion, E=Dazzle, R=Cosmic Radiance) - **✅ Ezreal:** All 5 abilities with detailed mechanics (Q=Mystic Shot, W=Essence Flux, E=Arcane Shift, R=Trueshot Barrage) - **✅ Yasuo:** All 5 abilities with complex mechanics (Passive=Way of the Wanderer, Q=Steel Tempest, W=Wind Wall, E=Sweeping Blade, R=Last Breath) - **✅ Jinx:** All 5 abilities with toggle mechanics (Q=Switcheroo!) and complex damage calculations - **✅ Samira:** All 5 abilities including complex ultimate mechanics (R=Inferno Trigger) **✅ Key Benefits Delivered:** - **Complete ability mechanics** for AI decision making and training ✅ - **Comprehensive data** including damage values, cooldowns, costs, ranges, cast times ✅ - **Multi-champion support** with consistent data format across all champions ✅ - **Real-time data** from official LoL Wiki with intelligent caching ✅ - **Production-ready** MCP tool integration with full error handling ✅ **Verification:** ✅ AbilitiesService returns comprehensive ability data for all 5 abilities (Passive, Q, W, E, R) for any champion with detailed mechanics, costs, and descriptions #### ✅ **Task 2.1.11: Enhanced get_ability_details MCP Tool with Details Tab** *(COMPLETED)* **Objective:** Add ability details MCP tool with "Details" tab content extraction using Selenium **Files:** `src/mcp_server/tools.py`, integration with `AbilitiesService` **Status:** ✅ **COMPLETED** - MCP tool for ability information with Details tab (December 2024) **✅ Implementation:** 1. **✅ Enhanced AbilitiesScraper:** Added Details tab interaction with Selenium - **Details Tab Clicking**: Implemented `scrape_ability_details_with_tab()` method - **CSS Selectors**: Uses `div.tabbertab[data-title="Details "]` (with trailing space) - **JavaScript Navigation**: DOM traversal with `nextElementSibling` for Details tabs - **Robust Error Handling**: Fallback to basic ability data when Details unavailable 2. **✅ Enhanced AbilitiesService:** Updated to use enhanced details when ability_slot provided - **Conditional Enhancement**: Enhanced details only when specific ability requested - **Data Integration**: Enhanced details appended after existing ability description - **Service Architecture**: Uses existing AbilitiesService with no breaking changes 3. **✅ Enhanced Details Structure:** Comprehensive ability mechanics extraction: - **Targeting Input**: "Direction", "Passive", "Auto" - how the ability is used - **Damage Classification**: Type ("Area damage", "Proc damage") and Sub-type ("Magic", "Physical") - **Counters**: Spell shield, projectile, parries interaction information - **Additional Notes**: Detailed mechanics and special interactions 4. **✅ MCP Tool Integration:** GetAbilityDetailsTool already supports enhanced details - **Ability Slot Parameter**: Optional parameter for specific ability enhancement - **Enhanced Response**: Includes enhanced_details section when ability_slot provided - **Backward Compatibility**: No breaking changes to existing API **✅ Verification Results:** - **✅ Taric E (Dazzle)**: targeting_input: "Direction", damage_classification: {type: "Area damage", sub_type: "Magic"}, counters: {projectile: "Not Blocked", spell_shield: "Blocked"} - **✅ Taric Passive (Bravado)**: targeting_input: "Passive", damage_classification: {type: "Proc damage", sub_type: "Magic"}, counters: {parries: "See Notes"} - **✅ Taric Q (Touch)**: targeting_input: "Auto", additional_notes with healing mechanics - **✅ Generic Implementation**: Works for any champion, not hardcoded - **✅ Error Handling**: Graceful fallback when Details tab unavailable **✅ Enhanced Details Format:** ```json { "enhanced_details": { "targeting_input": "Direction/Passive/Auto", "damage_classification": { "type": "Area damage/Proc damage", "sub_type": "Magic/Physical" }, "counters": { "spell_shield": "Blocked/Not Blocked", "projectile": "Blocked/Not Blocked", "parries": "See Notes" }, "additional_notes": ["List of detailed mechanics"] } } ``` **🔧 Technical Achievements:** - **Selenium Details Tab Interaction**: Automated tab clicking and content extraction - **CSS Selector Debugging**: Fixed `div.tabbertab[data-title="Details "]` (trailing space important) - **JavaScript DOM Navigation**: Reliable Details tab location using nextElementSibling - **Robust Implementation**: Works across all champions with proper error handling - **Enhanced MCP Tool**: Complete ability details with targeting, damage, and counter information #### ✅ **Task 2.1.12: Patch History Analysis Tool** *(COMPLETED)* **Objective:** Create new MCP tool to retrieve champion patch history from LoL Wiki **Files:** `src/data_sources/scrapers/patch_note_scraper.py`, `src/services/patch_note_service.py`, `src/mcp_server/tools.py` **Status:** ✅ **COMPLETED** - December 2024 **Implementation Details:** - **GetChampionPatchNoteTool**: New MCP tool for comprehensive patch history access - **PatchNoteScraper**: Inherits from BaseScraper with DOM navigation for patch extraction - **PatchNoteService**: Service layer for patch data management and caching - **CSS Selectors**: Uses `dt` elements for patch versions, `ul` siblings for changes - **Version Handling**: Supports both "4.12" and "V4.12" input formats - **Dynamic Champion Support**: Works with any champion without hardcoding **Technical Implementation:** ```python # MCP Tool Usage Examples get_champion_patch_note(champion_name="Taric") # Returns all patches get_champion_patch_note(champion_name="Taric", patch_version="V14.21") # Specific patch ``` **Verification Results:** - ✅ **32 patches retrieved** for Taric (V14.21 to V3.03) - ✅ **Specific patch queries** working correctly - ✅ **Non-existent patch handling** with appropriate messages - ✅ **MCP tool integration** fully functional - ✅ **Real HTML structure adaptation** from actual Wiki pages **Files Created:** - `src/data_sources/scrapers/patch_note_scraper.py` (247 lines) - Enhanced `src/services/patch_note_service.py` (251 lines) - Enhanced `src/mcp_server/tools.py` with GetChampionPatchNoteTool **Technical Achievements:** - **Advanced DOM Navigation**: Uses BeautifulSoup sibling traversal for multi-section patch data - **Version Format Flexibility**: Handles both user-friendly and internal version formats - **Complete Integration**: Full pipeline from scraper through service to MCP tool - **Robust Error Handling**: Graceful fallbacks with meaningful user feedback **Expected Benefits:** - **Comprehensive Patch History**: Access to complete champion change history - **Meta Analysis Support**: Historical balance data for trend analysis - **Training Data Enhancement**: Patch context for time-aware AI training - **User-Friendly API**: Clean MCP interface for external applications --- ### ✅ **Task 2.2 - Comprehensive Item Data System** *(COMPLETED)* **Overview:** Implement comprehensive item data system with differentiated data collection for completed items vs basic/epic items, assuming perfect item names initially. **🎯 Core Approach:** 1. **Perfect Name Assumption**: Initially assume users provide exact item names (e.g., "Echoes of Helia", "Kindlegem") 2. **Differentiated Extraction**: Extract different data sets based on item type (completed vs basic/epic items) with dynamic section detection **🔧 Architecture Requirements (Following Champion Pattern):** - **Service Layer Pattern**: Business logic services that orchestrate scrapers (like stats_service.py) - **Dynamic Section Detection**: Find sections by scanning page structure (like `_find_stats_section()`) - **Item Type Classification**: Distinguish completed/basic/epic items (like `_detect_dual_form_http_fast()`) - **Selenium Integration**: Handle expandable sections like cost analysis (like level dropdown interaction) - **Two MCP Tools**: `get_item_stats` and `get_item_patch_note` (split from original single tool) **🚫 No Hardcoding Rule:** All section titles, headers, and content discovered dynamically from page structure (means dynamic section detection, not avoiding CSS selectors entirely) **📊 Item Type Differentiated Data Extraction:** #### **Completed Items Data Requirements:** Based on screenshot examples (Echoes of Helia, etc.): - **Sidebar Info** (excluding recipe section) - **Recipe** section with components and build path - **Cost Analysis** (click expand button to show efficiency calculations) - **Notes** section with gameplay tips and usage information - **Map-Specific Differences** if they exist - **Similar Items** section for alternative choices - **Patch History** comprehensive change history #### **Basic/Epic Items Data Requirements:** Based on screenshot examples (Kindlegem, etc.): - **Sidebar Info** (excluding recipe and builds info sections) - **Recipe** section showing what it builds from - **Builds Info** section showing what items it builds into - **Cost Analysis** (click expand button to show efficiency calculations) - **Similar Items** section for alternatives at same tier - **Old Icons** section if available (historical versions) - **Patch History** comprehensive change history **📋 Sub-Tasks Breakdown:** #### ✅ **Task 2.2.1 - Individual Item Data Scraper** **REDESIGNED & COMPLETED** **Objective:** Create simplified item data scraper with clean output and perfect name assumptions **File(s):** `src/data_sources/scrapers/items/item_data_scraper.py` ✅ **REDESIGNED** **Status:** ✅ **REDESIGNED & COMPLETED** - Dramatically simplified with 65% code reduction and clean JSON output **Requirements (Differentiated Data Extraction):** 1. **Base Scraper Inheritance**: Inherit from `BaseScraper` (like stats_scraper.py) 2. **Item Type Detection**: `_classify_item_type_from_page()` method to detect completed vs basic/epic 3. **Dynamic Section Detection**: Scan page structure for sections without hardcoding section names 4. **Differentiated Data Extraction**: Extract appropriate data sets based on item type 5. **Selenium Integration**: Handle expandable cost analysis sections (click to expand) 6. **Sidebar Stats Parsing**: Extract item statistics from sidebar infoboxes **Data Extraction Specifications:** - **Completed Items**: sidebar info (no recipe), recipe, cost analysis (expand), notes, map differences, similar items - **Basic/Epic Items**: sidebar info (no recipe/builds), recipe, builds info, cost analysis (expand), similar items, old icons **Implementation Approach (Following Champion Pattern):** - Create new `item_stats_scraper.py` following champion scraper patterns - Dynamic section detection methods like `_find_section_by_content_scan()` - Item type classification from page structure analysis - Selenium WebDriver integration for expandable sections - Perfect item name assumption for URL generation **✅ REDESIGN Implementation Summary (December 2024):** - **Dramatically Simplified Architecture**: Reduced from 2268 lines to 782 lines (65% reduction) - **Simple Item Type Detection**: 3 reliable strategies instead of complex 5-strategy approach: - Main text analysis ("legendary item in", "basic item in", "epic item in") - wgCategories script parsing (most reliable) - Category links validation (fallback) - **Clean JSON Output**: No raw text/base_value format - structured data like champions - **Perfect Name Assumptions**: Direct URL building with intelligent normalization - **Champion Pattern Consistency**: Follows established champion scraper architecture - **Minimal Selenium Integration**: Only for truly expandable cost analysis sections - **Maintained Differentiated Extraction**: - Completed items: stats, recipe, cost_analysis, notes, similar_items - Basic/Epic items: stats, recipe, builds_info, cost_analysis, similar_items **✅ Key Achievements:** - **URL Normalization Fixed**: "Echoes of Helia" → "Echoes_of_Helia" (handles small words correctly) - **Type Detection Verified**: Correctly identifies legendary, epic, and basic items - **Integration Maintained**: Full compatibility with existing ItemService and MCP tools - **Clean Data Format**: Returns `{"name": "ability_power", "value": 35}` instead of raw text - **Gemini CLI Validation**: Confirmed "clear success" with "robust, well-designed" architecture **✅ Files Modified:** - `src/data_sources/scrapers/items/item_data_scraper.py` (complete rewrite: 782 lines) **✅ Verification Results:** - **Echoes of Helia**: ✅ Detected as "completed" (legendary item) - **Kindlegem**: ✅ Detected as "epic" item - **Long Sword**: ✅ Detected as "basic" item - **ItemService Integration**: ✅ No breaking changes, full compatibility maintained - **MCP Tools**: ✅ GetItemDataTool and GetItemPatchNoteTool working seamlessly #### **Task ✅ 2.2.2 - Item Patch History Scraper** **Objective:** Create item patch history scraper following champion patch_note_scraper.py pattern **File(s):** `src/data_sources/scrapers/items/item_patch_scraper.py` ✅ **COMPLETED** **Status:** ✅ **COMPLETED** - Item patch history scraper implemented with champion pattern consistency **Requirements (Following Patch Note Scraper Pattern):** 1. **Base Scraper Inheritance**: Inherit from `BaseScraper` (like patch_note_scraper.py) 2. **Dynamic Patch Section Detection**: `_find_patch_history_section()` method (like `_find_stats_section()`) 3. **Version Parsing**: Extract patch versions and changes (like `_is_valid_patch_version()`) 4. **Change Classification**: Buffs, nerfs, reworks (like patch change parsing) 5. **Item-Specific Navigation**: Handle item page structure vs champion pages **Implementation Approach (Following Champion Pattern):** - Use champion patch scraper pattern with item page adaptations - Dynamic patch history section discovery from page headers - Version validation and change extraction - Support both completed and basic/epic items - Comprehensive patch data extraction **Dependencies:** BaseScraper, champion patch scraper pattern **Deliverables:** ✅ **COMPLETED** - ✅ ItemPatchScraper class following exact champion pattern - ✅ Dynamic patch history section detection without hardcoding - ✅ Proper handling of nested UL HTML structure (item-specific) - ✅ Version parsing and validation for V14.19 and 14.19 formats - ✅ Change extraction from `<dl><dt>` and `<ul>` HTML structure - ✅ ItemPatchService following champion service pattern - ✅ GetItemPatchNoteTool MCP tool with direct service injection - ✅ Comprehensive test suite with 16/16 tests passing - ✅ Clean architecture with single responsibility principle - Dynamic patch history extraction - Version tracking and change classification #### ✅ **Task 2.2.3 - Item Services & MCP Tools Integration** *(COMPLETED)* **Objective:** Create item services and MCP tools following champion service and tool patterns **File(s):** `src/services/items/item_service.py` **(COMPLETED)**, `src/mcp_server/tools.py` (update) **Status:** ✅ **COMPLETED** **Requirements (Following Champion Service & MCP Tool Pattern):** 1. **ItemService**: Unified service combining stats and patch functionality (like ChampionService) 2. **Perfect Name Processing**: Direct item name to data extraction (no fuzzy matching initially) 3. **Two MCP Tools**: `get_item_data` and `get_item_patch_note` (split tools) 4. **Service Orchestration**: Coordinate scraper calls with error handling 5. **Error Handling**: `ItemNotFoundError` for invalid item names **Service Methods:** - `get_item_data(item_name, sections=None)` - Direct item stats retrieval - `get_item_patch_history(item_name, patch_version=None)` - Direct patch history - `_normalize_item_name(item_name)` - Name normalization for URL generation - `_extract_item_data(item_name)` - Data extraction coordination **MCP Tool Specifications:** ```python # Tool 1: Item datas get_item_data(item_name: str, sections: List[str] = None) # Assumes perfect names: "Echoes of Helia", "Kindlegem" # Returns: Differentiated data based on item type (completed vs basic/epic) # Tool 2: Item Patch History get_item_patch_note(item_name: str, patch_version: str = None) # Assumes perfect names with patch history extraction # Returns: Complete patch history for item ``` **Dependencies:** Tasks 2.2.1, 2.2.2 (both scrapers) **Deliverables:** - ItemService with direct processing - GetItemStatsTool and GetItemPatchNoteTool MCP tools - Complete integration following champion architecture --- **🎯 Direct Data Flow Example:** ``` User: get_item_stats("Echoes of Helia", sections=["stats", "recipe"]) ├── ItemService direct processing │ ├── Normalize name: "Echoes of Helia" → URL generation │ └── Generate URL: https://wiki.leagueoflegends.com/en-us/Echoes_of_Helia ├── ItemStatsScraper differentiated extraction │ ├── Detect item type: "completed item" from page structure │ ├── Extract sidebar stats (excluding recipe section) │ ├── Extract recipe section (components + build path) │ ├── Skip builds info (completed items don't have this) │ └── Return formatted response with differentiated data ``` **📊 Task 2.2 Summary:** - **Total Tasks:** 3 focused tasks with differentiated extraction - **Architecture:** Direct Processing + Champion Pattern (no fuzzy matching initially) - **Key Innovation:** Item type-specific data extraction with perfect names - **Technical Focus:** Dynamic section detection + Selenium expansion + Differentiated extraction - **Primary Deliverables:** Two MCP tools with direct name processing **🎯 Implementation Priority Order:** 1. **2.2.1 (Stats Scraper)** → **2.2.2 (Patch Scraper)** → **2.2.3 (Services & MCP)** **🚫 Critical "No Hardcoding" Examples (Dynamic Section Detection):** - ❌ **Wrong**: `soup.find("h2", text="Recipe")` - ✅ **Correct**: `self._find_section_by_header_scan(["recipe", "components", "build"])` - ❌ **Wrong**: `soup.select(".cost-analysis-section")` - ✅ **Correct**: `self._detect_expandable_sections()` + click to expand - ❌ **Wrong**: `{"attack_damage": 50, "health": 300}` - ✅ **Correct**: `self._parse_stat_fields_dynamically()` from sidebar - ❌ **Wrong**: `if item_name == "Moonstone Renewer":` - ✅ **Correct**: `item_type = self._classify_item_type_from_page(soup)` --- ### ✅ **Task 2.3: Runes Data from LoL Wiki** *(COMPLETED)* **Objective:** Implement rune system data extraction from LoL Wiki **Files:** `src/data_sources/scrapers/runes/`, `src/services/runes/`, `src/mcp_server/tools.py` **Status:** ✅ **COMPLETED** - Complete runes system with scrapers, services, and MCP tools **✅ Implementation Details:** - **RuneDataScraper**: Extracts sidebar (Path, Slot, Description, Range), Notes, and Strategy sections - **RunePatchScraper**: Extracts complete patch history for runes - **RuneService**: Service layer orchestrating rune data operations - **RunePatchService**: Service layer for rune patch history management - **2 New MCP Tools**: `get_rune_data` and `get_rune_patch_note` - **Perfect Name Support**: Direct rune name processing (e.g., "Summon Aery") - **Complete Integration**: Follows champion/item architecture patterns exactly **✅ Key Features Delivered:** - **Sidebar Information**: Path (Sorcery/Precision/etc), Slot (Keystone/Artifact/etc), descriptions, range data - **Notes Section**: Gameplay mechanics, interactions, and special behaviors - **Strategy Section**: Usage tips, champion synergies, and strategic advice - **Patch History**: Complete historical balance changes with version tracking - **Error Handling**: RuneNotFoundError, graceful fallbacks, mock data support - **Caching & Performance**: 24-hour TTL, rate limiting, async operations **✅ Production Ready:** - **MCP Compliance**: Full protocol compliance with proper schemas - **Type Safety**: Complete type hints and Pydantic validation - **Testing**: Comprehensive test suite covering all components - **Documentation**: Complete integration with existing architecture - **Resource Management**: Proper async cleanup and connection handling **✅ Files Created:** - `src/data_sources/scrapers/runes/rune_data_scraper.py` (370+ lines) - `src/data_sources/scrapers/runes/rune_patch_scraper.py` (360+ lines) - `src/services/runes/rune_service.py` (200+ lines) - `src/services/runes/rune_patch_service.py` (180+ lines) - `tests/test_rune_system.py` (280+ lines comprehensive test suite) - Updated `src/models/exceptions.py` (added RuneNotFoundError) - Updated `src/mcp_server/tools.py` (added 2 new MCP tools) --- ## Phase 3: Advanced Features & User Tools ### **Task 3.1: Player Search and Analytics** **Objective:** Implement player lookup and performance analysis **Status:** 🔄 **PENDING** - Player data and analytics **Purpose:** - **For taric_ai_agent**: High-ELO player data for imitation learning - **For general users**: Player statistics and performance tracking **Core Ideas:** - Player search by name, region, rank - Match history with detailed stats - Performance analytics (KDA, win rates, champion mastery) - Rank tracking and LP progression - Champion-specific performance metrics ### **Task 3.2: Build and Skill Order Recommendations** **Objective:** Provide intelligent build and skill recommendations **Status:** 🔄 **PENDING** - AI-driven recommendations **Purpose:** - **For taric_ai_agent**: Static build and skill order data for AI training - **For general users**: Meta build analysis and recommendations **Core Ideas:** - Meta build analysis from high-ELO players - Situational builds based on team composition - Skill order optimization for different scenarios - Counter-build suggestions against specific champions - Pro player build tracking and analysis ### **Task 3.3: Match Analysis Tools** **Objective:** Deep match analysis and insights **Status:** 🔄 **PENDING** - Match breakdown tools **Purpose:** - **For taric_ai_agent**: Training data from real match scenarios - **For general users**: Educational match analysis and insights **Core Ideas:** - Timeline analysis with key moments - Damage analysis (dealt, taken, healing) - Objective control and ward analysis - Team fight breakdown and positioning - Win condition analysis and patterns --- ## Phase 4: Training Data & AI Support ### **Task 4.1: Training Data Generation** **Objective:** Generate training datasets for AI projects **Status:** 🔄 **PENDING** - AI training pipeline **Purpose:** - **For taric_ai_agent**: RL training data and imitation learning datasets - **For general users**: Access to processed gameplay data **Core Ideas:** - State-action pairs for reinforcement learning - High-ELO demonstration data extraction - Scenario-specific training data generation - Reward calculation systems for AI feedback - Data validation and quality assurance ### **Task 4.2: Simulation Environment Support** **Objective:** Provide data backbone for lol_sim_env **Status:** 🔄 **PENDING** - Simulation data pipeline **Purpose:** - **For lol_sim_env**: Complete game rules and mechanics data - **For taric_ai_agent**: Accurate simulation environment for training **Core Ideas:** - Game mechanics formulas and calculations - Champion ability interactions and combos - Map data with terrain and objectives - Economic systems (gold generation, spending) - Balance data for accurate simulation --- ## Phase 5: Performance & Scalability ### **Task 5.1: Caching and Performance** **Objective:** Implement high-performance data access **Status:** 🔄 **PENDING** - Performance optimization **Core Ideas:** - Redis caching layer for frequently accessed data - Multi-level caching strategy - Cache invalidation on data updates - Performance metrics and monitoring - Response time optimization ### **Task 5.2: Database and Storage** **Objective:** Persistent storage for large datasets **Status:** 🔄 **PENDING** - Data persistence **Core Ideas:** - SQLAlchemy models for structured data - Data versioning across patches - Bulk operations for efficient updates - Migration system for schema evolution - Historical data preservation --- ## Phase 6: Integration & APIs ### **Task 6.1: Riot API Integration** **Objective:** Integrate official Riot Games API **Status:** 🔄 **PENDING** - Official data source **Purpose:** - **Hybrid approach**: Combine official API with Wiki scraping - **Real-time data**: Live match information and player stats - **Asset access**: Champion icons, ability images, and resources **Core Ideas:** - Riot API client with rate limiting - Official match and player data access - Asset downloads and management - Data synchronization between sources - Hybrid data validation and merging ### **Task 6.2: External Data Sources** **Objective:** Connect with external LoL data sources **Status:** 🔄 **PENDING** - Data source expansion **Core Ideas:** - Champion.gg for build and winrate data - OP.GG for player statistics - Lolalytics for advanced analytics - U.GG for tier lists and meta analysis - Data aggregation and normalization --- ## Phase 7: Production & Quality ### **Task 7.1: Testing and Validation** **Objective:** Comprehensive testing suite **Status:** 🔄 **PENDING** - Quality assurance **Core Ideas:** - Unit testing for all components - Integration testing for end-to-end functionality - Performance testing under load - Data validation and accuracy checks - Automated testing pipeline ### **Task 7.2: Deployment and Monitoring** **Objective:** Production-ready deployment **Status:** 🔄 **PENDING** - Production deployment **Core Ideas:** - Docker containerization - Health monitoring and alerting - Structured logging and tracing - Auto-scaling capabilities - Error tracking and reporting --- ## Phase 8: Documentation & Community ### **Task 8.1: Documentation System** **Objective:** Complete user and developer documentation **Status:** 🔄 **PENDING** - Documentation **Core Ideas:** - API documentation with examples - Usage guides and tutorials - Developer contribution guidelines - Integration documentation - Best practices and patterns ### **Task 8.2: Community Support** **Objective:** Tools for community developers **Status:** 🔄 **PENDING** - Community features **Core Ideas:** - SDK development for popular languages - Plugin system for extensibility - Example applications and demos - Community contribution workflows - Open source development guidelines --- ## ⚡ Performance Optimizations (v2.3.1) **Phase 2 Code Optimization** completed with significant performance improvements: ### 🏃‍♂️ **High-Impact Optimizations Implemented:** 1. **WebDriverWait Optimization** (`stats_scraper.py`) - **Before:** Fixed 1-second `time.sleep()` delays - **After:** Intelligent WebDriverWait conditions with fallback - **Impact:** ~50% faster level-specific stat scraping 2. **HTTP Request Caching** (`abilities_scraper.py`) - **Before:** Redundant page fetches for dual-form detection + single-form scraping - **After:** Fetch once, reuse cached soup object - **Impact:** ~40% faster single-form champion processing 3. **Improved Dual-Form Detection** (`abilities_scraper.py`) - **Before:** Selenium fallback for HTTP detection failures - **After:** Enhanced HTTP-based detection with better selectors - **Impact:** More reliable detection, fewer Selenium calls ### 📊 **Performance Metrics:** - **Scraping Speed:** 40-60% improvement for champion abilities - **Memory Usage:** Reduced BeautifulSoup object duplication - **Error Handling:** More specific error propagation - **Code Maintainability:** Cleaner separation of concerns ### 🔧 **Technical Improvements:** - Eliminated blocking operations where possible - Reduced redundant network requests - Better error handling without obscuring original errors - Consolidated text processing logic **See `docs/RECOMMENDATIONS.md` for detailed technical analysis and additional optimization opportunities.** --- ## 🚀 Getting Started To begin implementation: 1. **Start with Task 1.1** - Project setup and structure 2. **Focus on MVP** - Complete Phase 1 before moving to data sources 3. **Prioritize Imitation Learning** - Implement Phase 4 (IL Data Services) early 4. **Add Core Analysis** - Implement frame-by-frame analysis (Phase 5) 5. **Build Training Pipeline** - Add state-action pair generation (Phase 6) 6. **Test Early** - Implement tests alongside each task 7. **Iterate Fast** - Get basic functionality working before optimization 8. **Validate Continuously** - Test with real MCP clients throughout development Each task is designed to be completable in 2-4 hours of focused development time and produces a working, testable component that contributes to the overall system. The enhanced system now provides comprehensive LoL data access, sophisticated gameplay analysis, **ready-to-train imitation learning datasets**, and AI training capabilities that can support everything from individual player improvement to cutting-edge reinforcement learning research. ---