logo
Free, unlimited AI code reviews that run on commit
git-lrc git-lrc GitHub Install Now We'd appreciate a star git-lrc - Free, unlimited AI code reviews that run on commit | Product Hunt git-lrc - Free, unlimited AI code reviews that run on commit | Product Hunt

mcp-reasoner

Systematic reasoning server implementing Beam Search and Monte Carlo Tree Search (MCTS) for decision-making tasks. It evaluates thought processes through statistical analysis and provides tree-based reasoning paths.

Author

mcp-reasoner logo

parmarjh

MIT License

Quick Info

GitHub GitHub Stars 12
NPM Weekly Downloads 0
Tools 1
Last Updated 2026-02-19

Tags

mctsmcpreasonermcp reasonerreasoning serversearch mcts

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
  2. Beam Search: Evaluates and ranks multiple solution paths
  3. MCTS: Uses UCT for node selection and random rollouts
  4. Thought Scoring Based On:
  5. Detail level
  6. Mathematical expressions
  7. Logical connectors
  8. Parent-child relationship strength
  9. Process Management
  10. Tree-based state tracking
  11. Statistical analysis of reasoning
  12. 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.

See Also

`