Cero desventajas. 47% de ahorro promedio. Se paga solo en ~27 prompts. Auditado en 53 escenarios adversarios. Regla de veto de calidad: si no puede ahorrar sin pérdida, no toca tu tarea.
Consíguelo: Página de inicio · Gumroad ($9) · Power Bundle ($12) · SkillFlow Servidor MCP gratuito: PyPI · Smithery
Dev.to · PulseMCP · Smithery · MCPServers.org · Agensi.io · Indie Hackers · Awesome Manus AI
Manus cobra créditos por tarea. La mayoría de los usuarios desperdician entre el 30 y el 75% debido a cuatro comportamientos predeterminados:
Problema | Qué sucede | Desperdicio |
Enrutamiento de modelo incorrecto | Las tareas simples se ejecutan en modo Max cuando el modo Standard produce resultados idénticos | Hasta 5x de sobrepago |
Navegación lenta del navegador | Cada página toma de 8 a 45 segundos mediante llamadas a herramientas del navegador, quemando créditos en tiempo de espera | 150+ segundos por cada 10 URLs |
Sin detección de chat | Las tareas que cuestan $0 en modo Chat (preguntas y respuestas, lluvia de ideas, traducción) se ejecutan en modo Agente | 100% de sobrepago |
Inflación de contexto | Los tokens se acumulan en los pasos: cada paso posterior cuesta más que el anterior | Crecimiento exponencial |
Estos no son errores. Son configuraciones predeterminadas. El Power Stack anula las cuatro.
Related MCP server: ComputeGauge MCP
Esto es lo más importante que debes entender:
El Power Stack nunca puede empeorar las cosas. Tiene una regla de veto de calidad codificada: si una optimización redujera la calidad de salida incluso en un 1%, omite esa optimización por completo. En el peor de los casos, no hace nada. En el mejor de los casos, te ahorra un 75%.
No hay ningún escenario en el que instalar el Power Stack produzca un resultado peor que no tenerlo.
Analiza cada prompt antes de la ejecución y decide la ruta más barata que ofrece una calidad idéntica.
Clasificación de intención en 12 categorías (código, investigación, creativo, datos, traducción...)
Enrutamiento de modelos: Standard vs Max: solo usa Max cuando la complejidad realmente lo requiere
Detección de modo Chat: Enruta las preguntas y respuestas simples al modo Chat (costo $0)
Detección de lotes: Agrupa tareas relacionadas para reducir la sobrecarga
Higiene de contexto: Comprime el contexto acumulado para mantener los costos de tokens lineales
Reemplaza las lentas llamadas a herramientas del navegador con un kit de herramientas programático.
httpx + selectolax reemplaza la sobrecarga de Playwright (2 a 2000 veces más rápido)
Puente de cookies del navegador para sitios autenticados
Obtención paralela asíncrona: 10 URLs en 1.3 segundos en lugar de más de 150 segundos
Almacenamiento en caché de disco inteligente con TTL: las visitas repetidas son instantáneas
Credit Optimizer decide qué optimizar. Fast Navigation decide qué tan rápido ejecutarlo. Juntos, se multiplican:
Métrica | Sin Stack | Con Stack | Mejora |
Costo de Q&A simple | Precio completo | $0 (Modo Chat) | 100% ahorrado |
Investigación de 10 URLs | 150+ segundos | 1.3 segundos | 115x más rápido |
Tarea de web scraping | Créditos completos | 33% del original | 67% ahorrado |
Aplicación web full-stack | Créditos completos | 40% del original | 60% ahorrado |
Informe de investigación | 12 minutos | 2 minutos | 6x más rápido |
Promedio en todas las tareas | Línea base | 47% menos | 47% ahorrado |
El Power Stack cuesta $9 una sola vez (o $12 por el paquete con Fast Navigation). Así de rápido se paga solo:
Plan | Costo mensual | 47% desperdiciado | Desperdicio diario | Recuperación |
Plus ($39/mes) | 6,500 cr/día | ~3,055 cr/día | ~$0.44/día | ~20 prompts |
Max ($99/mes) | 16,250 cr/día | ~7,637 cr/día | ~$0.92/día | ~10 prompts |
Teams ($79/usuario/mes) | Varía | ~47% | Varía | 1–2 días |
Después de la recuperación, cada prompt te ahorra dinero. Durante el resto de tu suscripción a Manus. Para siempre.
Estimación de ahorro anual: ~$500–$1,000+ dependiendo del uso.
Copia los archivos de la skill a ~/skills/credit-optimizer/ y ~/skills/fast-navigation/
Ambos se activan automáticamente en cada tarea: no se requiere configuración
pip install mcp-credit-optimizer
python -m mcp_credit_optimizerAñade a tu configuración de MCP:
{
"mcpServers": {
"credit-optimizer": {
"command": "python",
"args": ["-m", "mcp_credit_optimizer"]
}
}
}Funciona con Claude Desktop, Cursor, Windsurf, Copilot y cualquier cliente compatible con MCP.
¿Por qué pagar si el servidor MCP es gratuito? El servidor MCP ahorra créditos cuando recuerdas llamarlo. La Manus Skill ahorra créditos en cada prompt automáticamente: no se necesita invocación manual. La Skill también incluye Fast Navigation (aumento de velocidad de 115x), que no está disponible como MCP.
Los 53 escenarios de prueba pasan con cero degradación de calidad:
Categoría | Escenarios | Pérdida de calidad |
Generación de código (Python, JS, React, SQL) | 12 | 0% |
Escritura creativa (blog, marketing) | 8 | 0% |
Análisis de datos (CSV, JSON, API) | 7 | 0% |
Investigación (síntesis de múltiples fuentes) | 6 | 0% |
Traducción y localización | 5 | 0% |
Corrección de errores y depuración | 5 | 0% |
Generación de documentación | 5 | 0% |
Tareas de intención mixta | 5 | 0% |
"Todo el mundo odia el sistema basado en créditos de Manus. Es una locura lo rápido que se queman los créditos". — Reddit, 90 votos positivos
"Usé 100,000 créditos en una semana y media. Toda mi asignación mensual se agotó". — Reddit
"Manus es tan caro que es absurdo. $200/mes y los créditos siguen agotándose". — Reddit
El Power Stack existe porque estas frustraciones son reales, y tienen solución.
Awesome Manus AI — Lista curada de recursos, herramientas y consejos de Manus AI
Guía de precios 2026 — Desglose completo de precios de Manus AI
Guía Standard vs Max — Cuándo usar cada modo
Tutorial — Guía completa para principiantes
Glosario — Más de 30 términos de Manus AI explicados
Hoja de ruta — Lo que viene a continuación
Canal | URL |
Página de inicio | |
Comprar individual ($9) | |
Comprar paquete ($12) | |
SkillFlow | |
PyPI (MCP gratuito) | |
Smithery | |
PulseMCP | |
Lista Awesome |
MIT: consulta LICENSE para obtener más detalles.
Creado por Rafael Silva · creditopt.ai
Analyze an AI agent prompt and return optimization recommendations.
Returns strategy, model recommendation, estimated credit savings, quality impact assessment, and efficiency directives.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The user's prompt/task description to analyze |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions what it returns but lacks details on side effects, idempotency, or prerequisites.
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?
Two sentences, front-loaded with action and result. Every word adds value.
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 one parameter and presence of output schema, description covers purpose and what is returned completely.
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?
With 100% schema coverage, baseline is 3. Description does not add meaning beyond the schema's description of the 'prompt' parameter.
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?
Clearly states it analyzes an AI agent prompt and returns optimization recommendations, listing specific return fields. Distinguishes from siblings get_golden_rules and get_strategy_for_task.
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?
Usage is implied but not explicitly stated. No guidance on when to use this tool versus siblings or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the 10 Golden Rules for credit optimization with ZERO quality loss.
Returns: The 10 audited golden rules with explanations
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden of behavioral disclosure. It does not confirm the tool is read-only or safe, nor does it describe any side effects, authorization needs, or rate limits. The claim of 'ZERO quality loss' pertains to the rules, not the tool's behavior.
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 only two sentences, front-loads the purpose, and contains no extraneous information. Every word serves a purpose.
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's simplicity (no parameters, clear retrieval) and the existence of an output schema, the description adequately explains what the tool returns: 'the 10 audited golden rules with explanations.' No further context seems necessary.
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?
The input schema has zero parameters and 100% coverage, so baseline is 4. The description does not need to add parameter-level detail, and it appropriately confirms no arguments are required.
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 clearly states 'Get the 10 Golden Rules for credit optimization with ZERO quality loss,' which specifies the exact resource (10 Golden Rules) and the action (Get). This distinguishes it from sibling tools analyze_prompt and get_strategy_for_task.
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 provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. It simply states what it returns.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the optimal strategy for a specific task type.
| Name | Required | Description | Default |
|---|---|---|---|
| task_type | Yes | One of: qa, code, research, content, data_analysis, media, automation |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states it 'gets' a strategy, but does not disclose any behavioral traits like whether it is read-only, if there are side effects, or what the output entails. Minimal behavioral context.
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?
Single sentence, concise and front-loaded. No unnecessary words.
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?
Simple tool with one param and output schema, but description does not mention output or any constraints. Lacks context about behavior and return value, despite output schema existing.
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 has one parameter with description listing allowed values, coverage is 100%. Description adds no additional meaning beyond schema. Baseline score of 3 is appropriate.
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?
Description clearly states the verb 'get' and resource 'strategy' for a specific task type. Input schema defines task_type with allowed values. It is distinct from sibling tools 'analyze_prompt' and 'get_golden_rules'.
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?
Usage is implied but not explicitly stated. No guidance on when to use this tool versus alternatives, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v5.0.0analyze_prompt1 field changedInput schema / properties / prompt / descriptionAdded value: +"The user's prompt/task description to analyze"get_strategy_for_task1 field changedInput schema / properties / task_type / descriptionAdded value: +"One of: qa, code, research, content, data_analysis, media, automation"v5.2.0analyze_promptget_golden_rulesget_strategy_for_taskScored across 3 tools
Each tool targets a distinct aspect of credit optimization: analyzing prompts, retrieving golden rules, and obtaining task-specific strategies. There is no overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case (analyze_prompt, get_golden_rules, get_strategy_for_task), making them predictable and easy to understand.
With 3 tools, the server is slightly minimal but still well-scoped for its advisory purpose. Each tool serves a clear role, and the count is reasonable given the focused domain.
The tools cover the core functionalities of analysis, reference rules, and strategy selection. A minor gap is the lack of an execution tool, but for an advisory server this is acceptable.
Reduces AI Agent token usage by 40% via three-stage SOP workflow.
Cost-optimized LLM model routing recommendations for autonomous AI agents
Enforce AI budgets before the model call and track cost per customer across 10 providers.
AI image, video, audio and text tools for creators. Pay per use with credits.
