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pqs-mcp-server

by io.github.OnChainAIIntel

optimize_prompt

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe prompt to optimize. Max 8000 characters.
api_keyNoPQS 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.

TDQS

A4.9/5.0
Behavior5/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

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.

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 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.

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TDQS

A4.8/5.0
Disambiguation5/5

The two tools have completely distinct purposes: one scores a prompt's quality, the other optimizes it. The descriptions clearly specify when to use each, so an agent is unlikely to confuse them.

Naming Consistency5/5

Both tools follow the verb_noun pattern (score_prompt, optimize_prompt), making the naming predictable and consistent across the set.

Tool Count4/5

With only 2 tools, the set is slightly below the typical 3-15 range, but it is well-scoped for the narrow domain of prompt scoring and optimization. Each tool serves a clear and complementary purpose.

Completeness5/5

The server covers the complete workflow: score a prompt to identify weaknesses, then optimize it to improve the score. There are no obvious missing operations or dead ends in this domain.

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