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PQS - Prompt Quality Score

Official

score_prompt

Score any prompt across 8 quality dimensions before sending to expensive models, returning a 0-80 score, A-F grade, and weakest dimension.

Instructions

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.

Input Schema

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

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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