CC-Meta (Claude Code Metaprompter)

CC-Meta lets you iterate on your Claude Code prompts without leaving the terminal. Instead of switching to the web client to test and refine prompts, you get instant AI feedback on clarity, specificity, and completeness right in your current workflow. This keeps you in context and speeds up the process of crafting effective prompts.

An MCP (Model Context Protocol) server that evaluates prompts using AI to provide detailed feedback on clarity, completeness, and effectiveness.

Before & After: Asking "Build a calculator app"

Without CC-Meta	With CC-Meta

Features

Multi-model support - Use any OpenAI or Anthropic model
Flexible API keys - Provide your own API key for each evaluation
Two tools available:
- ping - Test if the server is connected and working
- evaluate - Get AI-powered analysis of your prompts

Setup

Install dependencies:
```
npm install
# or
yarn install
```
Build the project:
```
npm run build
```

Configure your model and API key:Edit the .mcp.json file to set your preferred model and API key:

{
  "mcpServers": {
    "prompt-evaluator": {
      "command": "node",
      "args": ["./prompt-evaluator-mcp/start.js"],
      "env": {
        "PROMPT_EVAL_MODEL": "sonnet-4",  // or "o3", "opus-4"
        "PROMPT_EVAL_API_KEY": "your-api-key-here"
      }
    }
  }
}

Usage

Once configured, you have multiple ways to evaluate prompts:

Quick Slash Command (Recommended)

/meta Your prompt here without quotes

Direct MCP Function Calls

mcp_prompt-evaluator_ping()  # Test connection
mcp_prompt-evaluator_evaluate("Your prompt to evaluate")

Supported Models

OpenAI: o3 (o3-2025-04-16)
Anthropic: opus-4 (claude-opus-4-20250514), sonnet-4 (claude-sonnet-4-20250514)

The AI evaluation provides:

Score from 0-10
Specific strengths of your prompt
Areas for improvement
Suggested rewrites when needed
Analysis of:
- Clarity of intent
- Specificity of requirements
- Context provided
- Actionability
- Edge cases considered

Customization

The evaluation prompt is stored in src/prompt.ts and can be easily customized:

Edit the prompt template to change evaluation criteria
Modify the scoring rubric and weights
Adjust the output format
Add domain-specific evaluation rules

After making changes, rebuild with npm run build.

CC-Meta (Claude Code Metaprompter)