Promptest MCP
Click on "Deploy 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 MCP grade 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
Available Tools
4 toolsexplain_rubricExplain the prompting rubricA
Explains Promptest's 5-category rubric and the CRG-RIS study findings behind this MCP's guidance, without grading anything. Useful for understanding what the scores mean before you start.
Inputs: no named parameters.
get_prompting_historyGet your prompting history and statsA
Returns your personal history of graded prompts and logged outcomes, plus aggregate stats (average score, success rate, and — the key CRG-RIS finding — average turns-to-resolve when you named the root cause/fix location vs. when you didn't).
Inputs: limit.
grade_promptGrade a promptA
Scores a prompt you're about to send (or just sent) to an AI coding assistant against Promptest's validated 5-category rubric (Specificity, Clarity, Scope, Verification, Constraint Calibration). Returns an overall grade, category breakdown, and CRG-RIS-backed guidance. Returns a taskId — call log_outcome with that taskId once you know how it actually went, to build a personal track record of what your prompting style actually predicts.
Inputs: promptText (required).
log_outcomeLog a real-world outcome for a graded promptA
Records what actually happened after a prompt (previously scored via grade_prompt) was used — whether it succeeded, how many turns/messages it took to resolve, whether the fix stayed in scope, and whether the prompt named the bug's root cause and location. This is the data that lets Promptest MCP tell you what actually predicts good outcomes for YOUR prompting style, not just what the rubric guesses in the abstract.
Inputs: taskId (required), notes, succeeded, scopeCreep, +3 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
Each tool has a distinct and non-overlapping purpose: explaining the rubric, retrieving history, grading a prompt, and logging outcomes. No ambiguity.
All tool names follow a consistent verb_noun pattern with underscores (e.g., explain_rubric, grade_prompt), making them predictable.
Four tools is an appropriate and focused number for the domain of prompt assessment and tracking, covering the essential workflow without being too sparse or excessive.
The tool surface covers the complete cycle: learn the rubric, grade a prompt, log the outcome, and review personal history. No obvious gaps.
Maintenance
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Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceAn MCP server that evaluates prompts using AI to provide detailed feedback on clarity, completeness, and effectiveness.5ISC
- AlicenseBqualityDmaintenanceAn MCP server that automatically optimizes AI prompts using evolutionary algorithms, helping improve prompt performance, creativity, and reliability through iterative testing and refinement.123MIT
- AlicenseNot gradedqualityDmaintenanceThis MCP server provides research-backed prompt optimization tools and professional domain templates designed to improve AI performance through strategies like Tree of Thoughts and Medprompt. It enables users to analyze, auto-optimize, and refine prompts using advanced reasoning patterns and safety-critical alignment techniques.25MIT
- AlicenseAqualityCmaintenanceMCP server providing on-demand access to official prompting guides for building robust meta-prompts.23MIT