mcpField
M
MCP Reasoner

MCP Reasoner

Local
github.com/JacckAI & Agents277Updated 94w ago

About MCP Reasoner

MCP Reasoner is a reasoning MCP server for Claude Desktop that offers Beam Search and Monte Carlo Tree Search (MCTS) for complex problem solving. It is an early-stage project, and some algorithms are still in alpha.

Key features

  1. 01Switchable Beam Search and MCTS strategies
  2. 02Adjustable beamWidth and numSimulations parameters
  3. 03Tracks the quality of reasoning paths
  4. 04Analyzes the reasoning process
  5. 05Experimental mcts-002-alpha and mcts-002alt-alpha algorithms

Use cases

  1. 01Using Beam Search for straightforward problems
  2. 02Using MCTS to explore multiple paths on complex problems
  3. 03Comparing search strategies on Claude reasoning tasks

MCP support

The project follows the MCP protocol and runs as a reasoning tool within Claude Desktop.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Reasoner

A reasoning implementation for Claude Desktop that lets you use both Beam Search and Monte Carlo Tree Search (MCTS). tbh this started as a way to see if we could make Claude even better at complex problem-solving... turns out we definitely can.

Current Version:

v2.0.0

What's New:

Added 2 Experimental Reasoning Algorithms:

- `mcts-002-alpha`

    - Uses the A* Search Method along with an early *alpha* implementation of a Policy Simulation Layer

    - Also includes an early *alpha* implementation of Adaptive Exploration Simulator & Outcome Based Reasoning Simulator

*NOTE* the implementation of these alpha simulators is not complete and is subject to change

- `mcts-002alt-alpha`

    - Uses the Bidirectional Search Method along with an early *alpha* implementation of a Policy Simulation Layer

    - Also includes an early *alpha* implementation of Adaptive Exploration Simulator & Outcome Based Reasoning Simulator

*NOTE* the implementation of these alpha simulators is not complete and is subject to change

What happened to mcts-001-alpha and mcts-001alt-alpha?

Quite simply: It was useless and near similar to the base mcts method. After initial testing the results yielded in basic thought processes was near similar showing that simply adding policy simulation may not have an effect.

So why add Polciy Simulation Layer now?

Well i think its important to incorporate Policy AND Search in tandem as that is how most of the algorithms implement them.

Previous Versions:

v1.1.0

Added model control over search parameters:

beamWidth - lets Claude adjust how many paths to track (1-10)

numSimulations - fine-tune MCTS simulation count (1-150)

Features

  • Two search strategies that you can switch between:
    • Beam search (good for straightforward stuff)
    • MCTS (when stuff gets complex) with alpha variations (see above)
  • Tracks how good different reasoning paths are
  • Maps out all the different ways Claude thinks through problems
  • Analyzes how the reasoning process went
  • Follows the MCP protocol (obviously)

Installation

git clone https://github.com/frgmt0/mcp-reasoner.git 

OR clone the original:

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"],
    }
  }
}

Testing

[More Testing Coming Soon]

Benchmarks

[Benchmarking will be added soon]

Key Benchmarks to test against:

  • MATH500

  • GPQA-Diamond

  • GMSK8

  • Maybe Polyglot &/or SWE-Bench

License

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

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.