mcp-prompt-optimizer

Bubobot-Team/mcp-prompt-optimizer

3.3

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The MCP Prompt Optimizer is a professional-grade server designed to enhance AI prompt performance using advanced optimization strategies.

The MCP Prompt Optimizer is a cutting-edge server that leverages the Model Context Protocol (MCP) to deliver significant improvements in AI prompt performance. By utilizing a range of both basic and advanced optimization strategies, the server is capable of enhancing prompt clarity, specificity, and overall effectiveness. The tool is particularly beneficial for complex reasoning tasks, safety-critical applications, and scenarios requiring high accuracy. With a focus on research-backed methodologies, the MCP Prompt Optimizer integrates techniques such as Tree of Thoughts, Constitutional AI, and Medprompt to achieve performance improvements ranging from 15% to over 90%. Additionally, the server offers professional domain templates across various fields, making it a versatile tool for business analysis, product management, content creation, and more. The MCP Prompt Optimizer is designed for seamless integration with Claude Desktop and supports a wide range of platforms, ensuring accessibility and ease of use for developers and researchers alike.

Features

  • Basic and advanced prompt optimization strategies
  • Professional domain templates for various fields
  • Research-backed techniques for performance improvement
  • Seamless integration with Claude Desktop
  • Support for complex reasoning and safety-critical tasks

Usages

usage with Claude Desktop

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "python3",
      "args": ["/path/to/mcp-prompt-optimizer/prompt_optimizer.py"],
      "env": {}
    }
  }
}

Tools

  1. analyze_prompt

    Analyzes prompt quality and identifies issues

  2. optimize_prompt

    Applies specific optimization strategies

  3. auto_optimize

    Automatically selects optimal strategy

  4. get_prompt_template

    Returns basic templates

  5. advanced_optimize

    Applies research-backed strategies