MCP Reasoner

Example Use Cases

• 1

  Solve mathematical problems step-by-step using beam search to keep the most promising solution paths.

• 2

  Explore complex decision spaces with uncertainty using Monte Carlo Tree Search and UCT-based selection.

• 3

  Traceable, tree-based reasoning with thought scoring, progress monitoring, and statistical analysis.

Description

MCP Reasoner is a server implementation that provides systematic, tree-based reasoning using both beam search (for stepwise logical solutions) and Monte Carlo Tree Search (for complex, uncertain decision spaces). It includes thought scoring, evaluation metrics, progress monitoring, and statistical analysis of reasoning paths. The implementation is designed to integrate with Claude Desktop via the MCP protocol and is configurable for search strategy and beam width. It is useful for breaking down complex problems, optimizing strategies, and producing traceable reasoning paths.