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OnChainAIIntel

PQS - Prompt Quality Score

Official
by OnChainAIIntel
OverviewSchemaRelated ServersScoreDiscussions(1)
JavaScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.4.0

  • 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.

  • Average 4.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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.

  • 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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