Prompt Quality Score (PQS)
The fastest way to get better output from any AI model. Paste a prompt. Get a score from 0–100 across 8 dimensions. Get a rewritten version that scores higher. Ship better work.
The problem
Most prompts are bad, and you only find out after spending tokens. Output evaluation tools like RAGAS and G-Eval grade what the model already produced. PQS grades the prompt before it reaches the model. Pre-flight, not post-hoc.
How it works
PQS scores prompts across 8 dimensions (clarity, specificity, context, constraints, format, examples, role, evaluation criteria), grounded in 5 evaluation frameworks: PEEM, RAGAS, MT-Bench, G-Eval, ROUGE. You get a numeric score, a letter grade (A–F), and a rewritten prompt that addresses the gaps.
What you can do with it
- Score any prompt in the web app at promptqualityscore.com
- Use the MCP server in Claude Desktop, Cursor, or VS Code so any agent can score and rewrite prompts before execution
- Hit the API for batch scoring, comparison, and optimization
- Pay per call in USDC via x402 on Base, or subscribe via Stripe
Pricing
-Free tier with full scoring access.
-Pro at $19.99/mo for unlimited optimizations and API access.
-Per-call USDC starts at $0.025.
Install the MCP server
npx pqs-mcp-server
Or hit the hosted endpoint at https://promptqualityscore.com/api/mcp.
Links
PQS is to MCP servers what Google Search Console is to websites: an objective quality layer for the input side.