pqs-mcp-server
Server Details
PQS scores any prompt before the model runs. 8 dimensions. 5 frameworks. Pre-flight, not post-hoc.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- OnChainAIIntel/pqs-mcp-server
- GitHub Stars
- 2
- Server Listing
- PQS - Prompt Quality Score
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Tool Definition Quality
Average 4.9/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one scores a prompt across multiple dimensions, the other rewrites it for improvement. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern (optimize_prompt, score_prompt), making their function immediately clear.
Two tools are well-suited for a focused prompt quality utility. Adding more would risk scope creep, and fewer would be insufficient.
Core workflow is covered: score then optimize. Minor gap is lack of a standalone comparison tool, but the optimize tool includes before/after output, mitigating this.
Available Tools
2 toolsoptimize_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.
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: it requires a Pro subscription with an API key, costs against monthly quota, has ~6-8 second latency, and returns specific outputs including scores and comparisons. It also explains what happens if no API key is given.
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-structured with clear sections (main function, USE WHEN, DO NOT USE WHEN, REQUIRES, COST, LATENCY). It is appropriately sized, every sentence adds value, and key information is front-loaded.
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 the tool has only two parameters and one sibling, the description fully covers purpose, usage, requirements, latency, cost, and return values. No output schema exists, but the description explicitly states what is returned (optimized prompt, original and optimized scores, side-by-side outputs).
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% (both parameters described). The description adds value by explaining the API key format, subscription necessity, and providing direct links, as well as the max length for the prompt parameter. This goes beyond the schema's basic descriptions.
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 explicitly states the tool rewrites a prompt to improve PQS score and shows before/after comparisons. It clearly distinguishes from sibling tool 'score_prompt' by specifying that this tool optimizes rather than just scores.
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 includes explicit 'USE WHEN' and 'DO NOT USE WHEN' sections with clear conditions, such as after getting a low score from score_prompt or when the user asks for improvement. It also specifies when not to use it, like when the user only wants a score.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt text to score. Single prompt, not a conversation. Max 8000 characters. |
Tool Definition Quality
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 sec), and behavior on limit exceed (structured upgrade path). Returns per-dimension breakdown and weakest dimension. No annotations provided, so description carries full burden and does well.
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?
Front-loaded with main purpose, then structured sections for usage, cost, latency. Each sentence adds value, no fluff. Ideal length for a moderately complex tool.
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 no output schema, description details return values (score 0-80, grade, breakdown, weakest dimension). Covers cost, rate limits, latency, and behavior on limit. All relevant context for agent decision-making is present.
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?
Only parameter 'prompt' with maxLength. Schema coverage 100%. Description adds clarity: 'Single prompt, not a conversation' and reinforces max length. No enums, but sufficient for this simple input.
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?
Clear verb 'score' and resource 'prompt's quality'. Explicitly distinguishes from sibling 'optimize_prompt' by stating 'before sending to expensive model'. Usage conditions further clarify its role.
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?
Explicit 'USE WHEN' and 'DO NOT USE WHEN' sections with specific scenarios: workshopping, before expensive calls, diagnosing poor outputs, paste for feedback. Also specifies non-uses like writing prompts or conversational chat.
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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