RULER MCP

RULER MCP

MCP server for reading, aggregating, suggesting, and applying price rules (floors) from ActiveView with a confirmation flow, designed for AI agents to analyze and adjust monetization.

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README

RULER MCP — Price Rules (ActiveView)

Servidor MCP (JSON-RPC 2.0) focado 100% nas price rules da ActiveView: leitura, agregação, sugestão e aplicação de floors com fluxo de confirmação. Feito pra agentes de IA (CORTEX, Claude Desktop) analisarem e ajustarem a monetização junto com os dados de campanha.

As 5 tools

Tool Tipo O que faz
resumo_floors leitura Panorama do domínio: revenue total, eCPM ponderado, top 10 regras por revenue, REGRAS PROBLEMÁTICAS (match fora do desired, revenue zerado)
listar_price_rules leitura Todas as rules com floor, eCPM, revenue, impressões, match_rate, desired, país, device, uri, utm
historico_ajustes leitura Auditoria: quem mudou qual floor, quando, com snapshot anterior
sugerir_floor análise Sugestões SUBIR/DESCER com justificativa e confiança (match_rate vs desired) — não aplica nada
aplicar_floor AÇÃO Upsert na ActiveView — exige confirm=true; sem ele retorna preview e NÃO executa

Autenticação (duas camadas)

  1. Acesso ao MCP: Authorization: Bearer <token> — tokens em RULER_MCP_TOKENS
  2. ActiveView: cada tool recebe av_bearer como argumento — cada gestor usa a própria key. network e domain são informados pelo gestor.

Fluxo de segurança do aplicar_floor

  • Sem confirm: true → retorna preview e NÃO executa
  • Com confirm: true → aplica, grava snapshot anterior + mudança + actor no histórico
  • O agente deve SEMPRE mostrar o preview e só confirmar após aprovação explícita do gestor

Deploy no Railway

  1. Repo próprio (separado do RULER), conecta no Railway
  2. Variáveis: RULER_MCP_TOKENS, HIST_DB_PATH=/data/ruler-mcp-history.db
  3. Volume em /data (recomendado, pro histórico persistir)
  4. Generate Domain → endpoint: https://SEU-APP.up.railway.app/api/mcp

Requisitos

Node >= 22 (usa node:sqlite nativo pro histórico — zero compilação).

Fluxo de análise recomendado (pro agente)

  1. resumo_floors → panorama + problemas do domínio
  2. listar_price_rules → detalhe das regras relevantes
  3. sugerir_floor → propostas de ajuste
  4. Cruzar com dados de campanha (moodlr-ops: roas_cross, analise_campanhas)
  5. aplicar_floor sem confirm → preview → gestor aprova → confirm=true
  6. historico_ajustes depois → correlacionar mudança × resultado

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