The world's first named AI prompt quality score. Score, optimize, and compare LLM prompts before they hit any model. Free tier available. Built on PEEM, RAGAS, G-Eval, and MT-Bench frameworks. x402-native on Base.
Scored across 2 tools
The two tools have clearly distinct purposes: score_prompt evaluates prompt quality, while optimize_prompt rewrites prompts to improve scores. There is no overlap in their primary functions, and their descriptions emphasize when each should be used.
Both tool names follow a consistent verb_noun pattern (score_prompt, optimize_prompt) with past-tense verbs and a common noun. The naming is perfectly predictable and matches the tool's action.
With only two tools, the server feels minimal but appropriately scoped for a focused prompt-quality service. The count is borderline thin, yet the pair covers the core evaluation and improvement cycle.
The set provides a complete loop: score a prompt, then optimize it, with the optimization returning a new score and comparison. Minor gaps exist (e.g., no batch operation or history view), but these are hinted at in descriptions and not essential for the core purpose.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt to optimize. Max 8000 characters. | |
| api_key | No | PQS 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It thoroughly discloses the API key requirement, no-free-trial policy, cost, latency, return values, and exact handling of a missing key. This fully characterizes the behavior of a remote, subscription-gated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but scannable, using clear headers and bullet points. Every section (purpose, returns, use-when, do-not-use, requires, cost, latency) earns its place without unnecessary filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given it has no annotations or output schema, the description provides a complete picture: what the tool does, when to use it, what it returns, what prerequisites exist, and operational constraints. No significant gap remains for a 2-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the API key's subscription requirement, warning against guessing keys, and framing the prompt parameter as a previously pasted prompt for improvement. Minor redundancy but valuable operational detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific transformation ('rewrite a prompt to score higher on the PQS rubric') and adds a distinctive side-by-side comparison behavior. Clearly distinguishes from the sibling score_prompt by focusing on optimization rather than just scoring.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 with concrete triggers, including a direct reference to score_prompt for score-only requests. This gives the agent unambiguous guidance on tool selection and alternative usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt text to score. Single prompt, not a conversation. Max 8000 characters. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: it notes the tool is free, rate-limited (5/min, 10/day, 100/month), and takes ~2 seconds latency. It also explains the structured upgrade path response on rate-limit exceedance, providing critical operational context an agent needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with clear sections (USE WHEN, DO NOT USE WHEN, COST, LATENCY) and front-loaded with the primary purpose. Every sentence adds value, and the structure makes key information easy to parse for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having only one parameter and no output schema, the description explains the return value (0-80 score, A-F grade, per-dimension breakdown, weakest dimension), operation cost, and usage boundaries. This is sufficient for an agent to correctly invoke the tool and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers the single parameter 'prompt' with a clear description (single prompt, max 8000 chars). The tool description adds context about task-shaped prompts in the DO NOT USE section, but does not significantly elaborate beyond the schema. Given 100% schema coverage, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: scoring a prompt's quality across 8 dimensions before sending to an expensive model. It specifies the verb 'score' and the resource 'prompt', and lists the exact output dimensions (clarity, specificity, etc.), distinguishing it from the sibling 'optimize_prompt' which would alter the prompt rather than just evaluate it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit USE WHEN and DO NOT USE WHEN sections, listing concrete scenarios such as workshopping a prompt, pre-sending long prompts, or diagnosing poor output. It also clarifies when not to use it (e.g., when the user asks to write a prompt), implicitly guiding the agent to alternative actions like writing first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
optimize_prompt4 fields changedInput schema / properties / api_key / descriptionPrevious value: -"PQS API key. Get one at https://promptqualityscore.com?utm_source=mcp&utm_medium=schema_description&utm_campaign=2026-05-mcp-tools"New value: +"PQS 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."Input schema / properties / prompt / descriptionPrevious value: -"The prompt to optimize"New value: +"The prompt to optimize. Max 8000 characters."Input schema / properties / prompt / maxLengthAdded value: +8000Input schema / requiredPrevious value: -[
- "prompt",
- "api_key"
-]New value: +[
+ "prompt"
+]score_prompt2 fields changedInput schema / properties / prompt / descriptionPrevious value: -"The prompt to score"New value: +"The prompt text to score. Single prompt, not a conversation. Max 8000 characters."Input schema / properties / prompt / maxLengthAdded value: +8000optimize_prompt1 field changedInput schema / properties / verticalRemoved value: -{
- "description": "Domain context. Defaults to general.",
- "enum": [
- "software",
- "content",
- "business",
- "education",
- "science",
- "crypto",
- "general"
- ],
- "type": "string"
-}score_prompt1 field changedInput schema / properties / verticalRemoved value: -{
- "description": "Domain context for scoring. Defaults to general.",
- "enum": [
- "software",
- "content",
- "business",
- "education",
- "science",
- "crypto",
- "general"
- ],
- "type": "string"
-}compare_modelsgrade_promptcompare_models1 field changedInput schema / properties / api_key / descriptionPrevious value: -"PQS API key. Get one at promptqualityscore.com"New value: +"PQS API key. Get one at https://promptqualityscore.com?utm_source=mcp&utm_medium=schema_description&utm_campaign=2026-05-mcp-tools"grade_promptoptimize_prompt1 field changedInput schema / properties / api_key / descriptionPrevious value: -"PQS API key. Get one at promptqualityscore.com"New value: +"PQS API key. Get one at https://promptqualityscore.com?utm_source=mcp&utm_medium=schema_description&utm_campaign=2026-05-mcp-tools"compare_models1 field changedInput schema / properties / api_key / descriptionPrevious value: -"PQS API key. Get one at pqs.onchainintel.net"New value: +"PQS API key. Get one at promptqualityscore.com"optimize_prompt1 field changedInput schema / properties / api_key / descriptionPrevious value: -"PQS API key. Get one at pqs.onchainintel.net"New value: +"PQS API key. Get one at promptqualityscore.com"compare_modelsoptimize_promptscore_prompt
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