The MCP Reasoner server provides advanced reasoning capabilities for Claude Desktop through multiple strategies:
Utilize either Beam Search or Monte Carlo Tree Search (MCTS) for different complexity levels
Process sequential reasoning steps with thought tracking (thoughtNumber, totalThoughts, nextThoughtNeeded)
Solve mathematical problems and logical puzzles through step-by-step analysis
Analyze complex decision spaces and optimize strategies
Evaluate thoughts based on mathematical expressions and logical connectors
Manage tree-based reasoning paths with statistical monitoring
Select reasoning strategy via input parameters
Mentioned specifically as a benchmark where the MCTS strategy helped Claude perform well, scoring 6/10 on the public test versus 3/10 with beam search
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Reasonersolve this logic puzzle using MCTS: if A is taller than B, and B is taller than C, who is tallest?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: Perplexity MCP Server
Installation
git clone https://github.com/Jacck/mcp-reasoner.git
cd mcp-reasoner
npm install
npm run buildConfiguration
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
Search Strategy Selection
Beam Search: Evaluates and ranks multiple solution paths
MCTS: Uses UCT for node selection and random rollouts
Thought Scoring Based On:
Detail level
Mathematical expressions
Logical connectors
Parent-child relationship strength
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.
Resources
Looking for Admin?
Admins can modify the Dockerfile, update the server description, and track usage metrics. If you are the server author, to access the admin panel.