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OnChainAIIntel

PQS - Prompt Quality Score

Official
by OnChainAIIntel
OverviewSchemaRelated ServersScoreDiscussions(1)
JavaScript
Remote

GitHub Marketplace pqs-mcp-server MCP server

PQS MCP Server

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

Related MCP server: aipaygen-mcp

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

Available Tools

2 tools
optimize_promptAInspect

Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions.

USE WHEN:

  • The user got a low score from score_prompt and asks how to improve.

  • The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted.

  • The user is dissatisfied with output quality from a previous prompt and asks how to get better results.

  • score_prompt returned a suggestion to invoke this tool.

DO NOT USE WHEN:

  • The user just asked for a score (use score_prompt only — don't double up).

  • The user wants you to write a new prompt from scratch (write it directly).

REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call.

COST: Counted against your Pro subscription's monthly call quota.

LATENCY: ~6-8 seconds.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesThe prompt to optimize. Max 8000 characters.
api_keyNoPQS API key from a Pro subscription. Required. Format: pqs_live_… (32+ characters). Subscribe at https://promptqualityscore.com/pricing?utm_source=mcp&utm_medium=schema_description_v140&utm_campaign=2026-05-mcp-tools-v140 if you don't have one, or look up an existing key at https://promptqualityscore.com/account?utm_source=mcp&utm_medium=schema_description_v140&utm_campaign=2026-05-mcp-tools-v140.

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description thoroughly discloses behavior: it rewrites and shows comparisons, returns scores and outputs, requires an API key with subscription details, cost against quota, and latency of 6-8 seconds. Also explains the fallback when no API key is provided.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections and bullet points, and the main purpose is front-loaded. However, it could be slightly more concise; some repetition exists, such as 'score higher on the PQS rubric' appearing multiple times.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensive: explains return values despite no output schema, covers prerequisites, cost, latency, and when to use sibling. The tool's purpose and constraints are fully captured, leaving no significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters described. The description adds context: the API key format, subscription URL, and behavior when key is missing. This goes beyond the schema but is not essential since schema already covers basics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool rewrites a prompt to score higher on the PQS rubric and provides before/after comparisons. It distinguishes from the sibling 'score_prompt' by explicitly noting that 'score_prompt only scores' and this tool optimizes and compares.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'USE WHEN' and 'DO NOT USE WHEN' sections, listing specific conditions such as when the user asks for improvement or when score_prompt suggests it. Also clarifies when not to use it, e.g., if only a score is requested.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

score_promptAInspect

Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension.

USE WHEN:

  • The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?"

  • The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly.

  • The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing.

  • The user pastes a prompt and asks for feedback on it.

DO NOT USE WHEN:

  • The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify).

  • The prompt is conversational chat (this scores task-shaped prompts).

COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs.

LATENCY: ~2 seconds.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesThe prompt text to score. Single prompt, not a conversation. Max 8000 characters.

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses cost (free), rate limits (5/min, 10/day, 100/month), latency (~2 seconds), and behavior when limits exceeded (structured upgrade path). No annotations provided, so description carries full burden and does so comprehensively.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise and well-structured: starts with purpose and return values, then lists usage guidelines, cost, latency. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, no output schema), the description is complete. It explains return values, usage context, limitations, and behavior, fully informing an agent about when and how to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a description for the single 'prompt' parameter. The description adds extra context: specifies it must be a single prompt (not conversation) and max 8000 characters, which goes beyond the schema's description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scores prompt quality across 8 dimensions, returns a score, grade, breakdown, and weakest dimension. It distinguishes from sibling 'optimize_prompt' by focusing on evaluation before sending to expensive models, not optimization.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'USE WHEN' and 'DO NOT USE WHEN' sections, listing specific scenarios like workshopping prompts, about to send expensive prompts, or iterating. Excludes conversational chat and prompt writing tasks, which helps an agent decide when to invoke.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.8/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one scores a prompt, the other optimizes it. Their use cases and prerequisites are well-defined, so an agent cannot confuse them.

Naming Consistency5/5

Both tool names follow a consistent verb_noun pattern: 'score_prompt' and 'optimize_prompt'. The naming is uniform and predictable.

Tool Count4/5

With only two tools, the set is narrowly scoped to prompt scoring and optimization. While this is appropriate for the domain, slightly more tools (e.g., subscription management or rubric access) could enhance completeness without bloat.

Completeness4/5

The server covers the core workflow of scoring and optimizing prompts. However, it lacks tools for subscription management, retrieving the rubric, or performing batch operations, which are minor gaps given the stated dependencies and cost structure.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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