PQS - Prompt Quality Score

Tool Scores

• score_promptA
  4/5.0
  Behavior3/5

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

  With no annotations provided, description carries full burden. Discloses cost model ('Free tier — no payment required') and output format (grade, score, percentile) which substitutes for missing output schema. However, lacks disclosure on idempotency, data privacy (what happens to submitted prompts), rate limits, or side effects.

  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?

  Three sentences, zero waste. First sentence defines operation and returns, second states cost, third provides usage timing. Front-loaded with core functionality; excellent information density.

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

  Completeness4/5

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

  Adequately compensates for missing output schema by detailing return structure (grade, numeric score, percentile). Simple 2-parameter tool with 100% schema coverage requires minimal additional context. Lacks only minor behavioral details (rate limits, data retention) that would elevate to 5.

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

  Parameters3/5

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

  Schema coverage is 100% with both 'prompt' and 'vertical' fully documented in the schema. Description adds 'PQS (Prompt Quality Score)' acronym context but does not need to elaborate further given schema completeness. Baseline 3 appropriate for high-coverage schemas.

  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?

  States specific action (Score), resource (LLM prompt), methodology (PQS), and return values (grade A-F, score/40, percentile). Verb clearly distinguishes from siblings 'compare_models' (comparison) and 'optimize_prompt' (modification).

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

  Usage Guidelines4/5

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

  Provides explicit temporal guidance: 'Use this before sending any prompt to an LLM to check if it is worth running.' Clearly signals pre-validation use case. Does not explicitly name sibling alternatives but strongly implies evaluation-only intent.

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

• compare_modelsA
  4.2/5.0
  Behavior4/5

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

  Strong disclosure for a paid operation with no annotations: explicitly states cost ($0.50 USDC), payment mechanism (x402), judging methodology, and return structure. Would benefit from mentioning if the charge is immediate/reversible or if the operation is idempotent, but covers critical financial and methodological traits well.

  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?

  Three sentences with zero waste: sentence 1 defines the comparison methodology, sentence 2 specifies outputs, sentence 3 discloses cost. Information is front-loaded and logically ordered. Excellent density without verbosity.

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

  Completeness4/5

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

  Comprehensive for a 3-parameter tool with no output schema: description covers return values (winner, scores, recommendation) that would otherwise be unknown, and discloses financial requirements. Minor gap regarding error handling or async status, but adequate for safe invocation.

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

  Parameters3/5

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

  Schema coverage is 100%, so baseline applies. The description mentions 'prompt' in context but does not elaborate on parameter semantics beyond what the schema already documents (prompt content, vertical enum values, API key source). No additional parameter guidance provided in description prose.

  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?

  Excellent clarity: specifies exact action ('Compare'), specific models ('Claude vs GPT-4o'), methodology ('PQS', 'third model judge'), and output types. Clearly distinguishes from siblings optimize_prompt (single model optimization) and score_prompt (single model scoring) by emphasizing the head-to-head nature.

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

  Usage Guidelines4/5

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

  Implicitly guides usage through return value description ('winner', 'recommendation on which model to use'), indicating this is for decision-making between models. Lacks explicit 'when not to use' or direct comparison to siblings, but context is clear enough to infer appropriate use cases.

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

• optimize_promptA
  4.4/5.0
  Behavior4/5

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

  No annotations provided, so description carries full burden. Discloses return structure ('Returns the original score, an optimized version...'), cost destructor, and evaluation frameworks (PEEM, RAGAS, etc.). Lacks rate limiting or retry behavior details.

  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?

  Three efficient sentences with zero waste: action/returns first, cost second (critical barrier), usage guideline third. Front-loads the dual capability immediately.

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

  Completeness4/5

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

  Dense description adequately compensates for missing output schema by detailing return values (score, optimized version, breakdown). Covers cost and methodology. Could briefly define PQS or explain error states for full marks.

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

  Parameters3/5

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

  Schema coverage is 100%, establishing baseline 3. Description focuses on tool behavior rather than expanding parameter semantics, which is appropriate since schema fully documents 'prompt', 'vertical', and 'api_key'.

  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?

  Clear specific verbs ('Score AND optimize') and resource ('LLM prompt'). Capitalized 'AND' effectively distinguishes from sibling 'score_prompt' by emphasizing the dual action. Mentions specific methodology (PQS) and frameworks.

  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?

  Explicit when-to-use clause ('Use this when you want to improve a prompt before running it'). Critically includes cost warning ('Costs $0.025 USDC via x402') which acts as a guideline for when NOT to use or to prepare payment.

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