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Mcp Chain Of Draft Server

@bsmi021on 9 months ago
19 MIT
FreeCommunity
AI Systems
#ai#ai-tools#claude-ai#cod#mcp#mcp-server#model-context-protocol#modelcontextprotocol#server#tools#chain-of-draft#chainofdraft
Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code reviews, and implementation planning.

Overview

What is Mcp Chain Of Draft Server

The mcp-chain-of-draft-server is an AI-driven tool designed to assist developers in making informed decisions through a systematic and iterative refinement process of thoughts and designs.

Use cases

Use cases for the mcp-chain-of-draft-server include API design, architecture decision-making, code reviews, and implementation planning, allowing teams to enhance their development processes through structured reasoning.

How to use

To use the mcp-chain-of-draft-server, clone the repository, install the necessary dependencies, and configure the server settings in the ‘initialize.ts’ file. You can then utilize the Chain of Draft Protocol to manage your reasoning and critiques effectively.

Key features

Key features include iterative reasoning, thought history tracking, branching support for focused reviews, full TypeScript implementation with Zod validation, comprehensive error handling, and real-time logging for debugging and monitoring.

Where to use

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Content

MCP Chain of Draft Server 🧠

Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code reviews, and implementation planning.

🌟 Features

Core Capabilities

  • Iterative Reasoning: Systematic improvement through the Chain of Draft protocol
  • Thought History: Track and manage reasoning iterations
  • Branching Support: Focus reviews on specific reasoning steps
  • TypeScript Support: Full TypeScript implementation with Zod validation
  • Error Handling: Comprehensive error types and handling
  • Real-time Logging: Built-in debugging and monitoring system

🚀 Getting Started

Prerequisites

  • Node.js >= 16.0.0
  • npm >= 8.0.0

Installation

  1. Clone the repository:
git clone https://github.com/bsmi021/mcp-chain-of-draft-server.git
cd mcp-chain-of-draft-server
  1. Install dependencies:
npm install

Configuration

Simple server configuration in initialize.ts:

const serverConfig = {
    name: "chain-of-draft",
    version: "1.0.0",
}

💡 Usage Examples

Chain of Draft Protocol

const thoughtData = {
    reasoning_chain: ["Initial analysis of the problem"],
    next_step_needed: true,
    draft_number: 1,
    total_drafts: 3,
    is_critique: true,
    critique_focus: "logical_consistency"
};

🛠️ Development

Project Structure

src/
├── tools/                          # Specialized Tools
│   ├── chainOfDraft/              # Core Protocol
│   └── index.ts / # Entry Point
├── utils/                         # Utilities
└── index.ts                      # Entry Point

Starting Development Server

npm run dev

❓ FAQ

How does the Chain of Draft protocol work?

The protocol guides you through systematic improvement of your thinking through iterative drafts and focused critiques.

Can I customize the critique dimensions?

Yes! Each tool supports custom critique focuses tailored to your specific needs.

How many drafts should I plan for?

We recommend 3-5 drafts for most tasks, but you can adjust based on complexity.

🤝 Contributing

We welcome contributions! Please check our Contributing Guidelines.

👥 Community & Support

  • GitHub Issues - Report bugs or suggest features
  • Pull Requests - Submit your contributions
  • Documentation - Check our detailed docs

📝 License

MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Thanks to our contributors and early adopters
  • Special thanks to the MCP community
  • Inspired by systematic reasoning methodologies

Made with 🧠 by @bsmi021

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