This project provides a reasoning implementation for Claude Desktop, enhancing its complex problem-solving capabilities. It targets users looking to improve Claude's performance through advanced search strategies like Beam Search and Monte Carlo Tree Search (MCTS), offering experimental alpha algorithms for further exploration.

How It Works

The core of mcp-reasoner is its implementation of two primary reasoning strategies: Beam Search for straightforward tasks and Monte Carlo Tree Search (MCTS) for complex problems. It allows users to fine-tune MCTS parameters like beamWidth and numSimulations. Recent updates introduce experimental alpha versions of MCTS, incorporating A* Search or Bidirectional Search methods, coupled with a Policy Simulation Layer, Adaptive Exploration Simulator, and Outcome Based Reasoning Simulator. This approach aims to integrate search and policy in tandem, a common practice in advanced algorithms, to potentially yield better results in complex reasoning.

Quick Start & Requirements

Installation involves cloning the repository, running npm install, and then npm run build. The server needs to be configured within Claude Desktop by adding its command and arguments to the mcpServers section of the Claude Desktop configuration. Prerequisites include Node.js and npm. Official documentation, demos, or specific setup time estimates are not detailed in the README.

Highlighted Details

• Supports two distinct search strategies: Beam Search and MCTS with alpha variations.
• Enables fine-grained control over MCTS parameters (beamWidth, numSimulations).
• Maps and analyzes different reasoning paths taken by Claude.
• Adheres to the MCP protocol.
• Introduces experimental alpha algorithms (mcts-002-alpha, mcts-002alt-alpha) with A* or Bidirectional Search and simulation layers.

Maintenance & Community

The provided README does not contain information regarding maintainers, community channels (like Discord/Slack), sponsorships, or a roadmap.

Licensing & Compatibility

The project is licensed under the MIT License, which generally permits commercial use and modification with attribution.

Limitations & Caveats

The newly introduced mcts-002-alpha and mcts-002alt-alpha algorithms are explicitly marked as experimental and incomplete, subject to change. Comprehensive testing and benchmarking results are not yet available, making it difficult to assess performance claims or stability.