guru-pk-mcp

guru-pk-mcp

An MCP server that enables multi-round AI expert debates with dynamic expert generation, cross-debate, and Tufte-style infographic export.

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πŸ“š Language Versions | ε€šθ―­θ¨€η‰ˆζœ¬ | 言θͺžγƒγƒΌγ‚Έγƒ§γƒ³

🌐 English | πŸ‡¨πŸ‡³ δΈ­ζ–‡ | πŸ‡―πŸ‡΅ ζ—₯本θͺž


Guru-PK MCP Intelligent Expert Debate System

An AI expert debate system based on local MCP (Model Context Protocol), featuring dynamic expert generation architecture that intelligently creates the most suitable expert combinations based on questions for multi-round intellectual confrontation.

✨ Core Features

  • 🏭 Dynamic Expert Generation - Completely question-driven, generating dedicated expert combinations each time
  • 🌟 Unlimited Expert Pool - Breaking fixed expert limitations, supporting expert generation in any domain
  • πŸ”„ Multi-Round PK Process - Independent Thinking β†’ Cross-Debate β†’ Final Positions β†’ Wisdom Synthesis
  • 🎨 Tufte-Style Infographics - Transform expert debates into single-page dynamic infographics strictly following data visualization master Edward Tufte's design principles
  • πŸ€– Intelligent Division Architecture - MCP Host-side LLM handles intelligent analysis, MCP Server-side provides process guidance

🌐 Online Demo

πŸ‘‰ View Infographic Demo

This webpage displays Tufte-style dynamic infographics created using this MCP tool, intuitively showcasing the powerful capabilities of the expert debate system.

πŸš€ Quick Installation

1. Install Dependencies

Method 1: Using Installation Script (Recommended)

macOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Method 2: Install with pip (All Platforms)

pip install uv

Method 3: Download Installation Package

Download the installation package for your platform from UV Releases

2. Configure MCP Client

Recommended Method: Install from PyPI

{
  "mcpServers": {
    "guru-pk": {
      "command": "uvx",
      "args": ["--from", "guru-pk-mcp", "guru-pk-mcp-server"],
      "env": {
        "DATA_DIR": "~/.guru-pk-data"  // macOS/Linux: ~ directory, Windows: %USERPROFILE% directory
      }
    }
  }
}

Update Instructions:

  • When you need to update guru-pk-mcp to the latest version, run:

    uvx pip install --upgrade guru-pk-mcp
    
  • This command fetches and installs the latest released version from PyPI

  • If you encounter cache issues, you can force refresh:

    uvx --refresh-package guru-pk-mcp --from guru-pk-mcp python -c "print('βœ… UVX cache refreshed')"
    

Notes:

  • macOS users might need to use the full path: /Users/{username}/.local/bin/uvx
  • Windows users: ~ automatically resolves to user home directory (e.g., C:\\Users\\{username}), no manual modification needed

Development Method: Install from Source

{
  "mcpServers": {
    "guru-pk": {
      "command": "uvx", 
      "args": ["--from", "/path/to/guru-pk-mcp", "guru-pk-mcp-server"],
      "env": {
        "DATA_DIR": "~/.guru-pk-data"  // macOS/Linux: ~ directory, Windows: %USERPROFILE% directory
      }
    }
  }
}

Local Development Instructions:

  • For local development scenarios, if you need to refresh uvx cache, use make refresh-uvx
  • This command forces UVX to reinstall the local package, ensuring the use of latest code changes

🎯 For Claude Code Users (Recommended)

Using Custom Slash Command (More Elegant)

If you're using Claude Code, we recommend this simpler approach:

  1. Copy .claude/commands/guru-pk.md to your global ~/.claude/commands/ directory
  2. Use directly in any project: /guru-pk your question

Advantages:

  • βœ… No MCP server configuration needed
  • βœ… Start expert debates with one command
  • βœ… Cleaner, more elegant experience

Getting Started

Restart your MCP client, enter guru_pk_help to get help, or directly ask questions to start expert debates!

// 1. Natural language questions (most recommended usage)
Are there any directions in the field of generative AI that are particularly suitable for individual entrepreneurship? Please have three experts PK

// 2. Intelligent candidate expert generation (system automatic execution)
start_pk_session: Are there any directions in the field of generative AI that are particularly suitable for individual entrepreneurship?

// 3. Intelligent candidate expert generation (user limits expected expert scope)
start_pk_session: Are there any directions in the field of generative AI that are particularly suitable for individual entrepreneurship? Find two AI field experts and one famous individual entrepreneur to debate

πŸ’‘ Usage Tips

Starting Debates:

  • πŸ€– start_pk_session: direct question - Default high-efficiency batch processing mode (recommended)
  • πŸ”„ start_stepwise_pk_session: direct question - Traditional step-by-step dialogue mode

Tool Functions:

  • πŸ“‹ guru_pk_help - Get system introduction and detailed help
  • πŸ“„ export_session - Export session as Markdown file
  • 🎨 export_session_as_infographic - Export session as Tufte-style single-page dynamic infographic
  • πŸ“„ export_enhanced_session - Export enhanced analysis report
  • 🌍 set_language - Set expert reply language

πŸ“± Compatibility

Supports all MCP-compatible applications: Claude Desktop, Cursor, TRAE, DeepChat, Cherry Studio, etc.

🎯 Recommended Configuration

Most Recommended MCP Hosts:

  • πŸ’° Subscription-based MCP Hosts calculated by user requests - Such as Cursor and overseas TRAE
  • 🌟 Advantages:
    • Significant cost advantages: subscription billing calculated by user requests, not API call counts or token billing
    • Claude models have the best MCP support with excellent instruction-following capabilities

⚠️ Not Recommended Configuration

  • 🚫 TRAE Domestic Version - Built-in domestic models have sensitive word censorship issues that may interrupt expert debate processes, affecting user experience

πŸ› οΈ Technical Architecture

Intelligent Division Principles:

  • 🧠 MCP Host-side LLM: Responsible for complex semantic analysis and intelligent generation
  • πŸ”§ MCP Server-side: Provides concise process control and data management

Dynamic Expert Generation Flow

flowchart TD
    A[πŸ€” Raise Question] --> B[🧠 Intelligent Analysis]
    B --> C[πŸ‘₯ Generate Candidates]
    C --> D[πŸš€ Start Debate]
    
    A1[Ask system directly about any topic]
    B1[MCP Host-side LLM deeply analyzes question characteristics]
    C1[Dynamically create 3 most relevant experts]
    D1[Launch multi-round PK process]
    
    A -.-> A1
    B -.-> B1
    C -.-> C1
    D -.-> D1
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8
    style D fill:#fff3e0

πŸ”„ Debate Flow

Two Debate Modes:

πŸš€ Batch Processing Mode (start_pk_session) - Default Recommended

  • ⚑ High Efficiency: Generate all expert answers in one round, saving about 60% time
  • 🎯 Use Cases: Rapidly obtain multi-perspective analysis, efficient decision support

πŸ”„ Stepwise Mode (start_stepwise_pk_session) - Traditional Experience

  • 🎭 Interactive: Experts speak sequentially, allowing real-time adjustment and deep exploration
  • 🎯 Use Cases: Deep contemplation, enjoying the complete debate process

4-Round Debate Flow:

flowchart TD
    A[πŸ€” Independent Thinking] --> B[βš”οΈ Cross-Debate]
    B --> C[🎯 Final Positions]
    C --> D[🧠 Wisdom Synthesis]
    
    A1[Each expert independently analyzes the problem]
    B1[Experts mutually question and learn from each other]
    C1[Form their respective refined solutions]
    D1[Ultimate answer integrating all perspectives]
    
    A -.-> A1
    B -.-> B1
    C -.-> C1
    D -.-> D1
    
    B --> B2[Multi-round Interaction]
    B2 --> B
    
    style A fill:#e3f2fd
    style B fill:#fce4ec
    style C fill:#e8f5e8
    style D fill:#fff8e1
    style B2 fill:#f3e5f5

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