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OnChainAIIntel

PQS - Prompt Quality Score

Official
by OnChainAIIntel
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
JavaScript
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
score_promptA

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.

optimize_promptA

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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.8/5.0

Scored across 2 tools

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.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources