shubhampawar0901/IPL-analytics-mcp-server

Q: 1 IPL Analytics MCP Server Overview Develop an MCP (Model Context Protocol) server that provides intelligent SQL querying capabilities for the Indian Premier League (IPL) database, enabling natural language queries to be converted into SQL and executed against a comprehensive cricket analytics database.

Problem Context Dataset Specifications Database Type: SQLite Source: Kaggle IPL Database (https://www.kaggle.com/datasets/harsha547/ipldatabase/data) Coverage: 577 matches across seasons 2008-2016 Players: 469 detailed player profiles Schema Complexity: 15+ interconnected tables with rich relationships Key Database Entities Based on the schema diagram, the database contains:

Core Match Data:

Match details (venue, date, teams, outcomes) Ball-by-ball data with detailed events Toss decisions and match results Player Information:

Comprehensive player profiles (DOB, batting/bowling styles) Performance statistics and career data Team affiliations and roles Geographic & Venue Data:

City and country information Venue details and match locations Game Mechanics:

Wicket types and dismissal methods Batting and bowling styles Umpire and official information Primary Challenge Cricket analysts, sports journalists, data scientists, and IPL enthusiasts need to extract complex insights from this rich dataset, but they face several barriers:

Technical Barrier: Users must know SQL syntax and understand the complex relational schema Domain Knowledge Gap: Understanding cricket terminology and statistical relationships Query Complexity: Multi-table joins and aggregations required for meaningful insights Time Inefficiency: Manual SQL writing for exploratory data analysis Develop an MCP server with the following capabilities: Natural Language to SQL Translation Convert cricket-specific queries into optimized SQL Handle complex aggregations and multi-table joins SQL Agent Tools query_player_stats: Player performance analytics match_analysis: Match-level insights and comparisons team_performance: Team statistics and trends season_comparisons: Cross-season analysis head_to_head: Team vs team historical data Domain-Aware Query Processing Understand cricket terminology (strike rate, economy, etc.) Handle IPL-specific concepts (powerplay, death overs) Recognize player names, team names, and venues Sample Queries "Compare team performance in home vs away matches" "Find the most successful bowling combinations." "Historical head-to-head performance between CSK and MI" "Most impactful players in playoff matches" "Best performing players against specific teams" "Consistent performers across different match situations" "Player form analysis over recent matches" MCP Server Implementation Language: Python with SQLite integration Tools: Custom SQL agent tools for domain-specific queries NLP: Query understanding and SQL generation Optimization: Query performance