Simple Prompt Optimizer
A lightweight prompt optimization server based on the MCP protocol that uses frameworks such as OpenRouter and CO - STAR to quickly optimize prompts. It includes two tools: instant improvement and deep optimization.
rating : 2 points
downloads : 4.1K
What is Simple Prompt Optimizer MCP?
This is an intelligent tool specifically designed for optimizing AI prompts. It can analyze the original prompt you input, identify any vague, incomplete, or unclear parts, and then use an advanced optimization framework (such as the CO-STAR method) to reconstruct a more effective and precise prompt. Whether you're engaged in programming, writing, data analysis, or other AI-assisted tasks, this tool can significantly improve your communication efficiency with AI models.How to use Simple Prompt Optimizer MCP?
It's very simple to use: First, connect to this server through a client that supports the MCP protocol, such as Claude Desktop. Then you can directly call the optimization function. You only need to provide the original prompt and the task type, and the tool will automatically generate an optimized version for you. It supports two optimization modes: quick optimization and deep optimization to meet the needs of different scenarios.Applicable Scenarios
This tool is particularly suitable for the following scenarios: 1. When you need to have complex conversations with AI, ensure that the prompt is clear and accurate. 2. When writing code or technical documents, you need precise instructions. 3. For creative writing or content generation, you need guidance on style and tone. 4. In academic research or data analysis, you need rigorous query conditions. 5. Various tasks in daily work that require AI assistance.Main Features
Quick Optimization (instant_improve)
Suitable for daily tasks and quick fixes. Based on structured best practices, it rapidly improves the quality of prompts. You can specify the task type (such as programming, writing, etc.) and choose whether a detailed version is required.
Deep Optimization (deep_optimize)
Suitable for complex architectures, critical implementations, and debugging tasks. It performs a complete 'analyze - plan - rewrite' cycle: First, it analyzes the vagueness and missing constraints in the draft, then formulates a repair strategy, and finally rewrites it into a perfect prompt.
CO-STAR Framework Support
It uses the industry - verified CO-STAR method (Context, Objective, Style, Tone, Audience, Response) for reliable optimization, ensuring the completeness and effectiveness of prompts.
OpenRouter Integration
Access top AI models (such as Llama 3.1 405B, Claude 3.5 Sonnet, etc.) through OpenRouter to ensure optimization quality. It supports multiple model selections, including free options.
High - Performance Architecture
Built on FastMCP to achieve minimal latency. The optimized server architecture ensures fast responses and enhances the user experience.
Advantages
Fast and efficient: Faster response speed compared to other prompt optimization tools.
Reliable quality: Uses the verified CO-STAR framework to avoid random optimization.
Diverse models: Supports multiple top AI models through OpenRouter.
Easy to integrate: Standard MCP protocol, seamless integration with clients such as Claude Desktop.
Free options: Provides free model selections to reduce usage costs.
Limitations
Requires an API key: You must configure an OpenRouter API key to use it.
Relies on external services: The optimization quality is affected by the available models on OpenRouter.
Learning curve: The deep optimization function requires an understanding of its working principle to achieve the best results.
Configuration requirements: Requires a Python 3.10+ environment, which poses certain technical requirements for beginners.
How to Use
Environment Preparation
Ensure that your system has Python 3.10 or a higher version installed. It is recommended to use the uv package manager, but pip can also be used.
Get the Code
Clone the project from the GitHub repository to your local machine.
Install Dependencies
Use uv or pip to install the necessary dependency packages.
Configure the API Key
Create an OpenRouter account and obtain an API key. Then create a .env file in the project root directory and configure the key.
Configure Claude Desktop
Edit the Claude Desktop configuration file and add the MCP server configuration.
Start Using
Restart Claude Desktop, and you can now use the prompt optimization function in Claude.
Usage Examples
Programming Task Optimization
When you need to write code for a specific function, an optimized prompt can convey your requirements more clearly, reducing AI misunderstandings.
Content Creation Optimization
When generating content for a blog post or marketing copy, an optimized prompt can ensure consistency in style and tone.
Data Analysis Optimization
When dealing with complex data sets, a precise prompt can help the AI generate more accurate analysis results.
Frequently Asked Questions
Do I need to pay to use this tool?
What's the difference between instant_improve and deep_optimize?
Can I use my own AI model?
Will an optimized prompt definitely yield better results?
Can I use it on other clients besides Claude Desktop?
What should I do if I encounter configuration problems?
Related Resources
GitHub Repository
Project source code, latest version, and issue feedback
OpenRouter Official Website
Get an API key and view available models
MCP Protocol Documentation
Understand the technical details of the Model Context Protocol
CO - STAR Method Introduction
Learn the principles and applications of the CO - STAR prompt framework
Python Official Website
Download and install the Python environment
uv Package Manager
A fast and efficient Python package management tool

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