Which integrations are available for this server?

Allows the MCP server to use OpenAI's GPT models for generating and improving creative artifacts in the generate-critique-improve loop.

How do I use Prometheus-MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Prometheus-MCP Upgrade this creative coding output to professional level." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Prometheus-MCP

by gguatit

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Prometheus Main

Prometheus-MCP

Creative Director MCP

AI Creative Director that turns ordinary model output into expert-level work.

한국어 | English

한국어

Prometheus-MCP는 Claude, GPT, DeepSeek, MiniMax, Qwen, GLM 및 미래 모델이 생성한 크리에이티브 산출물을 전문가 수준으로 끌어올리는 Model Context Protocol 서버입니다. 단순한 문서 검색 시스템이 아니라, 생성 - 평가 - 개선 - 재생성 루프를 통해 품질을 지속적으로 향상시키는 AI Creative Director입니다.

대상 분야

웹 디자인 / UI 디자인 / UX 디자인
Three.js / React Three Fiber
VFX / 인터랙티브 경험
프론트엔드 애니메이션 / 크리에이티브 코딩
게임 개발

핵심 가치

전문가 패턴 라이브러리 - 12개 패턴 카테고리, 확장 가능한 구조
Critic Engine - 14개 품질 차원, 37개 규칙, 증거 기반 평가
자동 개선 루프 - 목표 점수 도달까지 반복
품질 인텔리전스 - 감사 가능한 점수 근거
AI 크리에이티브 디렉팅 - 패턴 선택, 개선 전략, 재생성

아키텍처

사용자 요청
  -> Planner
  -> Knowledge Collector
  -> Pattern Selector
  -> Prompt Enhancer
  -> Generation Provider
  -> Evidence Collector
  -> Critic Engine
  -> Quality Scoring
  -> Improvement Engine
  -> Regeneration Loop
  -> 최종 결과

모듈

모듈	역할
planner	도메인 탐지, 브리프 생성, 종료 정책
knowledge	외부 지식 수집 + 프롬프트 인젝션 방어
patterns	패턴 검증, 저장소, 가중치 기반 선택
critic	증거 수집(결정적 측정) + 규칙 평가 + 점수 + 감사 가능 근거
improver	부분/전체 재생성 전략, 수정 프롬프트
loop_controller	상태 머신 + 종료 정책(목표/최대반복/비용/시간/수익체감)
providers	역량 기반 라우터 (Stub, OpenAI 호환, Claude)
memory	세션 간 학습, 효과성 추적
history	세션 내 타임라인
telemetry	구조화 로그, 메트릭, 비용, 트레이싱, 비밀 마스킹
infrastructure	설정, 캐시, 보안
mcp	7 tools / 5 resources / 4 prompts

MCP 도구

도구	설명
direct_creative_work	브리프에서 생성-평가-개선 루프 실행
critique_artifact	산출물 평가 (점수, 강점, 약점, 제안)
improve_artifact	평가 기반 개선 계획 + 수정 프롬프트
list_patterns	패턴 목록 조회
get_pattern	패턴 상세 조회
recall_sessions	과거 세션 조회
get_quality_trends	품질 추세 분석

설치 및 실행

npm install
npm run build
npm start

테스트

npm test

설정

환경 변수로 런타임 설정을 덮어쓸 수 있습니다.

변수	설명
PROMETHEUS_TARGET_SCORE	목표 품질 점수 (기본 85)
PROMETHEUS_MAX_ITERATIONS	최대 개선 반복 (기본 5)
PROMETHEUS_MAX_COST_USD	비용 상한 (USD)
PROMETHEUS_DEFAULT_PROVIDER	기본 모델 제공자
PROMETHEUS_ENABLE_LLM_REASONING	LLM 추론 평가 활성화
PROMETHEUS_ALLOW_COMMUNITY_PATTERNS	커뮤니티 패턴 허용

모델 제공자 API 키는 해당 환경 변수(OPENAI_API_KEY, ANTHROPIC_API_KEY, DEEPSEEK_API_KEY 등)가 존재하면 자동 활성화됩니다.

기술 스택

TypeScript (ES Modules)
Node.js 20+
@modelcontextprotocol/sdk 1.29.0 (안정版)
zod v3 / ajv
Vitest

라이선스

MIT

Related MCP server: Prompt Auto-Optimizer MCP

English

Prometheus-MCP is a Model Context Protocol server that elevates creative output from Claude, GPT, DeepSeek, MiniMax, Qwen, GLM, and future models to expert-level quality. It is not a document retrieval system. It is an AI Creative Director that runs a generate - critique - improve - regenerate loop to continuously raise quality.

Target Domains

Web design / UI design / UX design
Three.js / React Three Fiber
VFX / interactive experiences
Frontend animation / creative coding
Game development

Core Value

Expert Pattern Library - 12 pattern categories, extensible structure
Critic Engine - 14 quality dimensions, 37 rules, evidence-bound evaluation
Auto Improvement Loop - iterates until target score is reached
Quality Intelligence - auditable score justifications
AI Creative Directing - pattern selection, improvement strategy, regeneration

Architecture

User request
  -> Planner
  -> Knowledge Collector
  -> Pattern Selector
  -> Prompt Enhancer
  -> Generation Provider
  -> Evidence Collector
  -> Critic Engine
  -> Quality Scoring
  -> Improvement Engine
  -> Regeneration Loop
  -> Final result

Modules

Module	Responsibility
planner	domain detection, brief formation, termination policy
knowledge	external knowledge collection + prompt injection defense
patterns	pattern validation, repository, weighted selection
critic	evidence collection (deterministic measurement) + rule evaluation + scoring + auditable justification
improver	surgical/full regeneration strategy, revision prompts
loop_controller	state machine + termination policy (target/maxIter/cost/wallClock/diminishing returns)
providers	capability-based router (Stub, OpenAI-compatible, Claude)
memory	cross-session learning, effectiveness tracking
history	intra-session timeline
telemetry	structured logs, metrics, cost, tracing, secret redaction
infrastructure	config, cache, security
mcp	7 tools / 5 resources / 4 prompts

MCP Tools

Tool	Description
direct_creative_work	runs generate-critique-improve loop from a brief
critique_artifact	evaluates an artifact (score, strengths, weaknesses, suggestions)
improve_artifact	builds improvement plan + revision prompt from a critique
list_patterns	lists available patterns
get_pattern	gets pattern detail
recall_sessions	recalls past sessions
get_quality_trends	analyzes quality trends

Install and Run

npm install
npm run build
npm start

Tests

npm test

Configuration

Environment variables override runtime config.

Variable	Description
PROMETHEUS_TARGET_SCORE	target quality score (default 85)
PROMETHEUS_MAX_ITERATIONS	max improvement iterations (default 5)
PROMETHEUS_MAX_COST_USD	cost cap (USD)
PROMETHEUS_DEFAULT_PROVIDER	default model provider
PROMETHEUS_ENABLE_LLM_REASONING	enable LLM reasoning in critique
PROMETHEUS_ALLOW_COMMUNITY_PATTERNS	allow community-trust patterns

Model provider API keys auto-enable their providers when the corresponding env var (OPENAI_API_KEY, ANTHROPIC_API_KEY, DEEPSEEK_API_KEY, etc.) is present.

Tech Stack

TypeScript (ES Modules)
Node.js 20+
@modelcontextprotocol/sdk 1.29.0 (stable)
zod v3 / ajv
Vitest

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

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