Content
# Reprompter MPC Server
An MCP (Model Context Protocol) server that analyzes prompts, compares them with relevant data, and uses OpenRouter (Claude 3.7 Sonnet) to refactor them.
## Features
- **Prompt Analysis**: Analyze prompts to identify strengths, weaknesses, and suggested improvements
- **Prompt Refactoring**: Refactor prompts using AI to improve their effectiveness
- **Context Integration**: Incorporate relevant context and chat history into the analysis and refactoring process
- **Technique Application**: Apply specific prompt engineering techniques to improve prompts
## Installation
1. Clone the repository
2. Install dependencies:
```
npm install
```
3. Create a `.env` file with your OpenRouter API key:
```
OpenRouter_API_KEY=your-api-key
```
4. Build the project:
```
npm run build
```
## Security
### API Key Management
To securely manage your OpenRouter API key:
1. **Never hard-code API keys** in files that might be committed to version control
2. **Use environment variables** to store sensitive credentials
3. **Add `.env` to your `.gitignore` file** to prevent accidentally committing it
4. **Consider using a secrets manager** for production deployments
### Environment Variable Setup
```bash
# On Windows
setx OPENROUTER_API_KEY "your-api-key"
# On macOS/Linux
export OPENROUTER_API_KEY="your-api-key"
```
## Usage
### Starting the Server
```
npm start
```
### Configuring in MCP Settings
A template configuration file is provided in `reprompter-mpc-config.template.json`. Copy this file to `reprompter-mpc-config.json` and update it with your specific paths:
```json
{
"mcpServers": {
"reprompter": {
"command": "node",
"args": ["build/index.js"],
"cwd": "PATH_TO_YOUR_REPROMPTER_MPC_DIRECTORY",
"env": {
"OpenRouter_API_KEY": "${OPENROUTER_API_KEY}"
},
"alwaysAllow": [
"analyze_prompt",
"refactor_prompt"
]
}
}
}
```
**Important:**
1. Replace `PATH_TO_YOUR_REPROMPTER_MPC_DIRECTORY` with the absolute path to your reprompter-mpc directory
2. Set up your OpenRouter API key using environment variables as described in the security section below
3. Add this configuration to your MCP settings file
### Available Tools
#### analyze_prompt
Analyzes a prompt and provides structured feedback on its strengths, weaknesses, and suggested improvements.
**Input Schema:**
```json
{
"prompt": "The prompt to analyze",
"context": "Optional context relevant to the prompt",
"chatHistory": [
{
"role": "user|assistant|system",
"content": "Content of the message",
"timestamp": "Optional timestamp"
}
]
}
```
**Output:**
```json
{
"strengths": ["strength1", "strength2", ...],
"weaknesses": ["weakness1", "weakness2", ...],
"suggestedTechniques": ["technique1", "technique2", ...],
"contextRelevance": {
"missingContext": ["item1", "item2", ...],
"irrelevantContext": ["item1", "item2", ...]
},
"complexity": "low|medium|high",
"clarity": "low|medium|high",
"specificity": "low|medium|high"
}
```
#### refactor_prompt
Refactors a prompt using AI to improve its effectiveness based on analysis and suggested techniques.
**Input Schema:**
```json
{
"prompt": "The prompt to refactor",
"analysis": {
// Optional analysis from analyze_prompt tool
"strengths": ["strength1", "strength2", ...],
"weaknesses": ["weakness1", "weakness2", ...],
"suggestedTechniques": ["technique1", "technique2", ...],
"contextRelevance": {
"missingContext": ["item1", "item2", ...],
"irrelevantContext": ["item1", "item2", ...]
},
"complexity": "low|medium|high",
"clarity": "low|medium|high",
"specificity": "low|medium|high"
},
"techniques": ["Optional specific techniques to apply"],
"context": "Optional context relevant to the prompt",
"chatHistory": [
{
"role": "user|assistant|system",
"content": "Content of the message",
"timestamp": "Optional timestamp"
}
]
}
```
**Output:**
The refactored prompt text.
## Example Usage
```javascript
// Example of using the analyze_prompt tool
const analysis = await use_mcp_tool({
server_name: "reprompter",
tool_name: "analyze_prompt",
arguments: {
prompt: "Tell me about the history of artificial intelligence.",
context: "The user is a beginner in AI concepts."
}
});
// Example of using the refactor_prompt tool
const refactoredPrompt = await use_mcp_tool({
server_name: "reprompter",
tool_name: "refactor_prompt",
arguments: {
prompt: "Tell me about the history of artificial intelligence.",
analysis: analysis, // Result from analyze_prompt
techniques: ["Chain-of-Thought", "Few-Shot Examples"],
context: "The user is a beginner in AI concepts."
}
});
```
## Contributing
Contributions are welcome! Here's how you can contribute to this project:
1. Fork the repository
2. Create a new branch (`git checkout -b feature/your-feature-name`)
3. Make your changes
4. Commit your changes (`git commit -m 'Add some feature'`)
5. Push to the branch (`git push origin feature/your-feature-name`)
6. Open a Pull Request
Please make sure your code follows the existing style and includes appropriate tests.
## GitHub Features
### Issues
If you encounter any bugs or have feature requests, please [open an issue](https://github.com/Mnehmos/reprompter-mcp/issues) on GitHub. When creating an issue, please provide:
- A clear and descriptive title
- A detailed description of the issue or feature request
- Steps to reproduce the issue (if applicable)
- Any relevant logs or error messages
- Your environment (OS, Node.js version, etc.)
### Pull Requests
Pull requests are welcome! Please see the [Contributing](#contributing) section for guidelines.
## License
ISC - See [LICENSE](./LICENSE) file for details.
