What is Mcp Reasoner

MCP Reasoner is a systematic reasoning MCP server implementation designed for Claude Desktop, featuring both Beam Search and Monte Carlo Tree Search (MCTS) capabilities to enhance decision-making processes.

Use cases

Use cases for mcp-reasoner include solving mathematical problems, logical puzzles, step-by-step analysis, complex problem decomposition, decision tree exploration, and strategy optimization.

How to use

To use mcp-reasoner, clone the repository from GitHub, navigate to the project directory, install the necessary dependencies using npm, and configure it in the Claude Desktop settings by specifying the command and arguments for the server.

Key features

Key features include dual search strategies (Beam Search and MCTS), thought scoring and evaluation, tree-based reasoning paths, statistical analysis of the reasoning process, and compliance with the MCP protocol.

Where to use

MCP Reasoner can be utilized in various fields such as artificial intelligence, mathematics, logical problem-solving, and any domain requiring systematic reasoning and decision-making.

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

Beam Search

• Maintains fixed-width set of most promising paths
• Optimal for step-by-step reasoning
• Best for: Mathematical problems, logical puzzles

Monte Carlo Tree Search

• 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.