Skip to main content
Glama

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 a taskId.

  • 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 the taskId from grade_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 build

2. 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_API_KEY

Anthropic (Claude)

OPENAI_API_KEY

OpenAI

GOOGLE_API_KEY

Google (Gemini)

OPENROUTER_API_KEY

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 tools
explain_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

    A4.4/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool has a distinct and non-overlapping purpose: explaining the rubric, retrieving history, grading a prompt, and logging outcomes. No ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with underscores (e.g., explain_rubric, grade_prompt), making them predictable.

    Tool Count5/5

    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.

    Completeness5/5

    The tool surface covers the complete cycle: learn the rubric, grade a prompt, log the outcome, and review personal history. No obvious gaps.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      An MCP server that evaluates prompts using AI to provide detailed feedback on clarity, completeness, and effectiveness.
      5
      ISC
    • A
      license
      Not graded
      quality
      D
      maintenance
      This 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.
      25
      MIT