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A systematic reasoning MCP server implementation for Claude Desktop featuring both Beam Search and Monte Carlo Tree Search (MCTS) capabilities.

概要

A systematic reasoning MCP server implementation for Claude Desktop featuring both Beam Search and Monte Carlo Tree Search (MCTS) capabilities. * Dual search strategies: * Beam search with configurable width * MCTS for complex decision spaces * Thought scoring and evaluation * Tree-based reasoning paths * Statistical analysis of reasoning process * MCP protocol compliance * Maintains fixed-width set of most promising paths * Optimal for step-by-step reasoning * Best for: Mathematical problems, logical puzzles * Simulation-based exploration of decision space * Balances exploration and exploitation * Best for: Complex problems with uncertain outcomes Monte Carlo Tree Search allowed Claude to perform really well on the Arc AGI benchmark (scored 6/10 on the public test), whereas beam search yielded a (3/10) on the same puzzles. For super complex tasks, you'd want to direct Claude to utilize the MCTS strategy over the beam search. 1.

README

MCP Reasoner

A systematic reasoning MCP server implementation for Claude Desktop featuring both Beam Search and Monte Carlo Tree Search (MCTS) capabilities.

Features

  • Dual search strategies:
    • Beam search with configurable width
    • MCTS for complex decision spaces
  • Thought scoring and evaluation
  • Tree-based reasoning paths
  • Statistical analysis of reasoning process
  • MCP protocol compliance

Installation

git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run build

Configuration

Add to Claude Desktop config:

{
  "mcpServers": {
    "mcp-reasoner": {
      "command": "node",
      "args": ["path/to/mcp-reasoner/dist/index.js"],
    }
  }
}

Search Strategies

  • Maintains fixed-width set of most promising paths
  • Optimal for step-by-step reasoning
  • Best for: Mathematical problems, logical puzzles
  • Simulation-based exploration of decision space
  • Balances exploration and exploitation
  • Best for: Complex problems with uncertain outcomes

Note: Monte Carlo Tree Search allowed Claude to perform really well on the Arc AGI benchmark (scored 6/10 on the public test), whereas beam search yielded a (3/10) on the same puzzles. For super complex tasks, you’d want to direct Claude to utilize the MCTS strategy over the beam search.

Algorithm Details

  1. Search Strategy Selection
    • Beam Search: Evaluates and ranks multiple solution paths
    • MCTS: Uses UCT for node selection and random rollouts
  2. Thought Scoring Based On:
    • Detail level
    • Mathematical expressions
    • Logical connectors
    • Parent-child relationship strength
  3. Process Management
    • Tree-based state tracking
    • Statistical analysis of reasoning
    • Progress monitoring

Use Cases

  • Mathematical problems
  • Logical puzzles
  • Step-by-step analysis
  • Complex problem decomposition
  • Decision tree exploration
  • Strategy optimization

Future Implementations

  • Implement New Algorithms
    • Iterative Deepening Depth-First Search (IDDFS)
    • Alpha-Beta Pruning

License

This project is licensed under the MIT License - see the LICENSE file for details.

GitHub で README 全文を見る

インストール

This server does not publish a one-line install command.

Open the repository installation guide MCP 初心者?mcpskill.org のガイドから始めましょう →