Solves Boggle boards and checks word boggleability using Trie data structures and constraint-satisfaction algorithms.
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This Python project offers a suite of tools for the classic Boggle word game, demonstrating advanced programming concepts. It leverages a highly efficient Trie data structure for rapid prefix lookups and employs constraint-satisfaction with backtracking for combinatorial search problems, such as determining if a word can be formed from Boggle dice. Additionally, it integrates with the Model Context Protocol (MCP) to function as an external tool for large language models, enabling AI-powered workflows to solve Boggle puzzles or validate words directly within chat interfaces.
Key Features
01Provides a standalone Trie data structure for fast dictionary lookups and prefix checking
02Determines if a word can be spelled with official Boggle dice using constraint satisfaction
03Solves Boggle boards using depth-first search and Trie-based prefix pruning
040 GitHub stars
05Supports custom word lists and variable board sizes
06Offers an MCP server for seamless integration with large language models
Use Cases
01Quickly finding all valid words on a Boggle board
02Verifying if a given word is valid to be formed using Boggle dice
03Integrating Boggle game logic into AI applications and LLM-powered conversational agents
