Integrates with Discord to provide real-time champion information, build recommendations, and community meta insights for bots and applications.
Enables integration with JetBrains IDEs via the MCP protocol to provide League of Legends data and statistics directly within the developer environment.
Provides a data backbone for building React-based web and mobile applications focused on League of Legends analytics and data visualization.
Extracts and provides community-driven meta insights and strategy trends from League of Legends subreddits.
Connects to official Riot Games APIs to provide live match data, player analytics, and comprehensive game statistics.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LoL Data MCP ServerWhat is the best build for Ahri in the current patch?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
š§ PROJECT UNDER MAJOR RESTRUCTURE š§
LoL Data MCP Server is currently undergoing comprehensive restructuring and enhancement. This project will cover significantly more functionality than originally planned.
š Current Status: Major Expansion in Progress
This project is being actively restructured to become a comprehensive League of Legends data ecosystem that will include:
šÆ Planned Coverage Areas (Under Development)
Phase 1: Core Data Infrastructure ā”
Champion Data System: Complete champion statistics, abilities, and patch history
Item Data System: Item statistics, build paths, and patch tracking
Runes & Masteries: Complete rune system integration
Game Mechanics: Damage calculations, scaling formulas, and interactions
Phase 2: Advanced Analytics š
Meta Analysis: Patch-by-patch meta evolution tracking
Build Optimization: AI-powered optimal builds for different scenarios
Champion Synergies: Advanced team composition analysis
Performance Metrics: Win rates, pick/ban statistics, and trend analysis
Phase 3: AI Integration š¤
Training Data Generation: Structured datasets for machine learning
Game State Recognition: Real-time game state parsing and analysis
Decision Support: AI-powered recommendations for in-game decisions
Simulation Environment: Complete LoL simulation for AI training
Phase 4: Real-Time Services ā”
Live Match Data: Real-time match tracking and analysis
Player Analytics: Individual player performance tracking
Meta Predictions: AI-powered meta shift predictions
Community Integration: Discord bots, web APIs, and mobile apps
Phase 5: Advanced Features š
Video Analysis: Automatic highlight detection and analysis
Voice Integration: Voice-activated champion information and builds
AR/VR Support: Immersive data visualization for coaching
Esports Analytics: Professional match analysis and statistics
š ļø Technical Scope Expansion
Data Sources Integration
League of Legends Wiki: Primary source for comprehensive game data
Riot Games API: Official live data and statistics
Community Platforms: Reddit, Discord, and forums for meta insights
Esports Platforms: Professional match data and analytics
Streaming Platforms: Popular streamer builds and strategies
Technology Stack Enhancement
Backend: FastAPI, WebSocket, async/await patterns
Data Processing: BeautifulSoup, Selenium, pandas, numpy
AI/ML: TensorFlow, PyTorch, scikit-learn for analytics
Caching: Redis for high-performance data caching
Database: PostgreSQL for structured data, MongoDB for flexible schemas
API Integration: RESTful APIs, GraphQL, WebSocket real-time updates
Integration Capabilities
IDE Integration: Cursor, VS Code, JetBrains via MCP protocol
Discord Bots: Real-time champion information and builds
Web Applications: React/Vue frontends for data visualization
Mobile Apps: React Native for on-the-go access
CLI Tools: Command-line utilities for developers
Game Overlays: In-game information overlays
šÆ Project Timeline
Current Phase: Core Infrastructure Development
Expected Completion: Rolling releases with major milestones every 2-4 weeks
Full Feature Set: Estimated 6-12 months for complete ecosystem
š Related Projects
This MCP server will serve as the data backbone for:
LoL Simulation Environment: AI training environments
Taric AI Agent: Specialized support champion AI
Community Tools: Discord bots, web apps, and mobile applications
Research Projects: Academic and professional esports analytics
š Development Status
ā Currently Implemented:
Basic MCP server infrastructure
Champion statistics scraping (with level-specific data)
Champion abilities extraction
Item patch history tracking
Real-time wiki data integration
š Under Active Development:
Advanced item data system
Comprehensive patch tracking
Enhanced data accuracy and validation
Performance optimization and caching
š Planned Features:
Complete runes and masteries system
Build recommendation engine
Meta analysis and tracking
Real-time match integration
AI-powered insights and recommendations
ā” This project represents a significant expansion beyond the original scope and will become a comprehensive League of Legends data ecosystem serving multiple AI, analytics, and community applications.
š Stay tuned for regular updates as we build the most comprehensive LoL data service available.