PQS MCP Server

MCP serverAI & models

Your AI can score any prompt before the model runs it, so weak prompts get caught up front instead of after the fact. PQS checks each prompt across 8 dimensions using 5 scoring frameworks. Because it works pre-flight, prompt quality is known before a run starts.

Use PQS MCP Server in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add PQS MCP Server and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the score prompt tool from PQS MCP Server

Details

Available today. Use it from your connected AI after setup.

After adding it, send one of your prompts through PQS to see its score before the model processes it.

PQS MCP ServerStart free

What your AI can do with it

  • Score any prompt before the model runs it
  • Evaluate prompts across 8 quality dimensions
  • Check prompts against 5 scoring frameworks
  • Catch weak prompts before the model sees them
  • Get prompt quality feedback up front instead of after the fact

Install PQS MCP Server

The server’s own address, for the clients that take one directly. Or connect Ahel once and every client you use reads it from one address, with the account kept on Ahel rather than in each client’s config.

  • Claude Code

    claude mcp add --transport http --scope user pqs-mcp-server 'https://promptqualityscore.com/api/mcp'

    Run it once in your project, then open /mcp to approve any sign-in the server asks for.

  • Claude Desktop

    https://promptqualityscore.com/api/mcp

    Add a custom connector in Settings, paste this address, and approve the sign-in.

  • Cursor

    cursor://anysphere.cursor-deeplink/mcp/install?name=pqs-mcp-server&config=eyJ1cmwiOiJodHRwczovL3Byb21wdHF1YWxpdHlzY29yZS5jb20vYXBpL21jcCJ9

    Open the link and Cursor adds the server at that address.

  • ChatGPT

    https://promptqualityscore.com/api/mcp

    In Settings, enable Developer mode, create an MCP app, and paste this address. Your plan and workspace must allow custom apps.

  • Codex

    codex mcp add pqs-mcp-server --url 'https://promptqualityscore.com/api/mcp'

    Run it once, then sign in with codex mcp login pqs-mcp-server if the server asks for an account.

From the project's README

As published by onchainaiintel/pqs-mcp-server in README.md.

Score prompt quality before it reaches any AI model. An MCP server for PQS.

Score and optimize LLM prompts before they hit any model. Built on PEEM, RAGAS, MT-Bench, G-Eval, and ROUGE.

Install

Claude Desktop (stdio)

Add to your config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "pqs": {
      "command": "npx",
      "args": ["-y", "pqs-mcp-server"]
    }
  }
}

Remote (HTTP)

Use this when your MCP client supports streamable-HTTP transport (no local npm install required):

{
  "mcpServers": {
    "pqs": {
      "url": "https://promptqualityscore.com/api/mcp"
    }
  }
}

Smithery

smithery mcp add onchaintel/pqs

Tools

score_prompt (Free, no API key required)

Returns a 0-80 score, A-F grade, full 8-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. Rate-limited per IP: 5/min, 10/day, 100/month.

Low- and mid-band scores also include a structured suggestion field with a message, a next_tool pointer to optimize_prompt, and a subscribe URL the consuming LLM can paraphrase back to the user.

Example output (low-band score, suggestion attached):

{
  "pqs_version": "2.0",
  "prompt": "analyze this wallet",
  "score": 9,
  "out_of": 80,
  "grade": "F",
  "dimensions": {
    "clarity": 2,
    "specificity": 1,
    "context": 1,
    "constraints": 1,
    "output_format": 1,
    "role_definition": 1,
    "examples": 1,
    "cot_structure": 1
  },
  "weakest_dimension": "specificity",
  "powered_by": "PQS — promptqualityscore.com",
  "suggestion": {
    "message": "This prompt scored 9/80 (F) — significant room to improve. The optimize_prompt tool rewrites it and shows side-by-side outputs from a frontier model, so you can see the impact. optimize_prompt is part of PQS Pro ($19.99/mo, 1,000 calls/mo). Subscribe at https://promptqualityscore.com/pricing?utm_source=mcp&utm_medium=suggestion_v140&utm_campaign=2026-05-mcp-tools-v140.",
    "next_tool": "optimize_prompt",
    "subscribe_url": "https://promptqualityscore.com/pricing?utm_source=mcp&utm_medium=suggestion_v140&utm_campaign=2026-05-mcp-tools-v140"
  }
}

If the per-IP rate limit is hit, the response is a structured rate_limit_exceeded payload with subscribe and account URLs.

optimize_prompt (Pro subscription required)

Rewrites a prompt to score higher and runs both versions through a frontier model so the user can see the before/after output. Returns the optimized prompt, before/after dimension scores (with totals), improvement_pct, and side-by-side sample outputs.

Pro subscription required ($19.99/mo, 1,000 calls/mo, includes batch + A/B comparison). Subscribe at promptqualityscore.com/pricing.

If the API key is missing, invalid, or on the Free tier, the tool returns a structured error pointing the user at the right URL. No silent failures, no inventing keys. Errors emitted:

  • api_key_required: no api_key argument was sent
  • api_key_invalid: key not recognized
  • subscription_required: key is valid but Free tier (subscribe to upgrade)
  • rate_limited: per-minute burst limit reached (Pro is rate-limited per minute, not per month) or temporary upstream capacity issue
  • service_unavailable: upstream 5xx

Quality Gate Pattern

Use PQS as a pre-inference quality gate:

const score = await fetch("https://promptqualityscore.com/api/score/free", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ prompt: userPrompt })
});
const { score: pqsScore } = await score.json();
if (pqsScore < 56) throw new Error("Prompt quality too low. Improve and retry.");

Grade D or below (under 56/80) means the prompt will waste inference spend.

x402 (legacy pay-per-call)

The MCP tools in this package use the SaaS API-key model. A separate x402-native pay-per-call path is available via the canonical PQS HTTP API (no API key, caller settles USDC on Base on-chain). For x402 integration, see the canonical pricing and discovery artifacts at promptqualityscore.com.

Self-hosting

Override the PQS backend URL with the PQS_BASE environment variable:

PQS_BASE=https://your-pqs-host.example.com npx pqs-mcp-server

Defaults to https://promptqualityscore.com.

Built by

OnChainIntel, @OnChainAIIntel promptqualityscore.com

Tools it offers (2)

What this server listed when Ahel dialed its public endpoint in Sep 2026, with no key and no account of yours. The names are the server’s own.

  • score_prompt
  • optimize_prompt

Signals

GitHub stars
2
Last commit
May 2026
Weekly downloads
49
Advanced
Delivery
pqs-mcp-server MCP server → your Ahel connector (mcp.ahel.ai) → your AI.
Item type
mcp-server
Key
io-github-onchainaiintel-pqs-mcp-server
Source
github.com/onchainaiintel/pqs-mcp-server
Hosted endpoint
https://promptqualityscore.com/api/mcp