Promptest MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Promptest MCPgrade this prompt: 'Fix the login bug by validating the token in auth.js line 42.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Promptest MCP
An MCP server that grades your AI prompts against Promptest's validated 5-category rubric, tracks what actually happened when you used them, and gives you guidance grounded in the CRG-RIS study of what really predicts good outcomes — not just what looks good on paper.
Why this exists
Promptest's rubric reliably scores prompt quality. But a CRG-RIS evaluation of that rubric found something more specific and more useful: overall score didn't predict cost or scope discipline on a real coding task — but whether the prompt named the bug's root cause and exact fix location did, by a wide margin (37–41% lower cost, and the only variant that avoided a security-flagged over-broad fix).
This MCP server bakes that finding directly into its guidance, and — because it's an MCP tool that lives inside your actual coding sessions — it can track your own real outcomes over time, not just score prompts in isolation the way the web app does.
Related MCP server: Prompt Auto-Optimizer MCP
Tools
grade_prompt— scores a prompt against the 5-category rubric, returns a grade, category breakdown, and guidance. Returns ataskId.log_outcome— record what actually happened: did it work, how many turns did it take, did the fix stay in scope, did you name the root cause/location. Pass thetaskIdfromgrade_prompt.get_prompting_history— your personal history and stats, including the CRG-RIS-validated comparison: average turns when you named the root cause/location vs. when you didn't.explain_rubric— explains the rubric and the CRG-RIS findings without grading anything.
Install
1. Build
git clone https://github.com/cortexresearch/promptest-mcp.git
cd promptest-mcp
npm install
npm run build2. Add to your MCP client config
Claude Code (.claude/settings.json or via claude mcp add):
{
"mcpServers": {
"promptest": {
"command": "node",
"args": ["/absolute/path/to/promptest-mcp/dist/index.js"],
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}Claude Desktop (claude_desktop_config.json): same shape, under mcpServers.
3. Set your API key
Set exactly one of these in the env block above, matching whichever provider you want as the judge:
Env var | Provider |
| Anthropic (Claude) |
| OpenAI |
| Google (Gemini) |
| OpenRouter (any model) |
The server auto-detects which provider to use from whichever key is set. To force a specific provider or model, set PROMPTEST_PROVIDER (anthropic | openai | google | openrouter) and/or PROMPTEST_MODEL.
Your key never leaves your machine except to call the provider you chose. This server has no backend, sends nothing to Promptest or Cortex Research Group, and doesn't phone home.
Where your data lives
History is stored locally at ~/.promptest/history.json — plain JSON, nothing hidden, delete it any time to start fresh.
Background
Promptest — the web app this rubric was built for
CRG-RIS study — the research this MCP's guidance is grounded in, including the honestly-reported finding that the rubric's overall score alone didn't predict real-world cost, and what did
License
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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