MCP Reasoner

此包由 MCPFlow 打包并发布到npm仓库。

为Claude Desktop 实现的基于系统推理的MCP服务器，采用波束搜索和思维评估。

安装与使用

直接使用npx运行:

npx @mcpflow.io/mcp-mcp-reasoner

或者先安装后使用:

# 安装
npm install @mcpflow.io/mcp-mcp-reasoner

# 使用
npx @mcpflow.io/mcp-mcp-reasoner

使用方法

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

工具函数

processInput

处理输入并确保正确的类型

参数:

• input: 输入数据

registerTheTool

注册工具

参数:

handleRequests

处理请求

参数:

• request: 请求对象

processThought

使用选定的策略处理思想

参数:

• request: 处理请求

getStats

获取推理统计信息

参数:

getStrategyMetrics

获取策略度量

参数:

getCurrentStrategyName

获取当前策略名称

参数:

getBestPath

获取最佳路径

参数:

clear

清除状态

参数:

setStrategy

设置策略

参数:

• beamWidth: 束宽
• strategyType: 策略类型
• numSimulations: 模拟次数

getAvailableStrategies

获取可用策略

参数:

processThought

处理思想

参数:

• request: 请求

getBestPath

获取最佳路径

参数:

clear

清除

参数:

getMetrics

获取度量

参数:

clear

清除

参数:

processThought

处理思想

参数:

• request: 请求

getBestPath

获取最佳路径

参数:

clear

清除

参数:

getMetrics

获取度量

参数:

clear

清除

参数:

processThought

处理思想

参数:

• request: 请求

getBestPath

获取最佳路径

参数:

clear

清除

参数:

getMetrics

获取度量

参数:

clear

清除

参数:

原始信息

• 开发者: Jacck
• 版本: 1.0.0
• 许可证: MIT License
• 原始仓库: Jacck/mcp-reasoner

原始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.