Keine Nachteile. 47 % durchschnittliche Ersparnis. Zahlt sich in ca. 27 Prompts selbst ab. Geprüft in 53 gegnerischen Szenarien. Qualitäts-Veto-Regel: Wenn eine Einsparung nicht ohne Qualitätsverlust möglich ist, wird die Aufgabe nicht angerührt.
Holen Sie es sich: Landing Page · Gumroad ($9) · Power Bundle ($12) · SkillFlow Kostenloser MCP-Server: PyPI · Smithery
Dev.to · PulseMCP · Smithery · MCPServers.org · Agensi.io · Indie Hackers · Awesome Manus AI
Manus berechnet Kredite pro Aufgabe. Die meisten Benutzer verschwenden 30–75 % aufgrund von vier Standardverhaltensweisen:
Problem | Was passiert | Verschwendung |
Falsches Modell-Routing | Einfache Aufgaben laufen im Max-Modus, obwohl Standard identische Ergebnisse liefert | Bis zu 5-fache Überzahlung |
Langsame Browser-Navigation | Jede Seite benötigt 8–45 Sekunden über Browser-Tool-Aufrufe, was Kredite durch Wartezeit verbrennt | 150+ Sekunden pro 10 URLs |
Keine Chat-Erkennung | Aufgaben, die im Chat-Modus $0 kosten (Q&A, Brainstorming, Übersetzung), laufen im Agenten-Modus | 100 % Überzahlung |
Kontext-Aufblähung | Tokens sammeln sich über Schritte hinweg an — jeder nachfolgende Schritt kostet mehr als der vorherige | Exponentielles Wachstum |
Das sind keine Fehler. Das sind Standardeinstellungen. Der Power Stack überschreibt alle vier.
Related MCP server: ComputeGauge MCP
Das ist das Wichtigste, was man verstehen muss:
Der Power Stack kann die Dinge niemals verschlechtern. Er verfügt über eine fest programmierte Qualitäts-Veto-Regel: Wenn eine Optimierung die Ausgabequalität auch nur um 1 % verringern würde, überspringt er diese Optimierung vollständig. Im schlimmsten Fall tut er nichts. Im besten Fall spart er Ihnen 75 %.
Es gibt kein Szenario, in dem die Installation des Power Stacks ein schlechteres Ergebnis liefert als ohne ihn.
Analysiert jeden Prompt vor der Ausführung und entscheidet über den günstigsten Pfad, der identische Qualität liefert.
Absichtsklassifizierung über 12 Kategorien (Code, Recherche, Kreatives, Daten, Übersetzung...)
Modell-Routing: Standard vs. Max — verwendet Max nur, wenn die Komplexität es wirklich erfordert
Chat-Modus-Erkennung: Leitet einfache Q&A in den Chat-Modus ($0 Kosten)
Batch-Erkennung: Gruppiert verwandte Aufgaben, um den Overhead zu reduzieren
Kontext-Hygiene: Komprimiert angesammelten Kontext, um Token-Kosten linear zu halten
Ersetzt langsame Browser-Tool-Aufrufe durch ein programmatisches Toolkit.
httpx + selectolax ersetzt Playwright-Overhead (3.000–2.000x schneller)
Browser-Cookie-Brücke für authentifizierte Seiten
Asynchrones paralleles Abrufen — 10 URLs in 1,3 Sekunden statt 150+ Sekunden
Intelligentes Festplatten-Caching mit TTL — wiederholte Besuche erfolgen sofort
Credit Optimizer entscheidet, was optimiert werden soll. Fast Navigation entscheidet, wie schnell es ausgeführt werden soll. Zusammen multiplizieren sie sich:
Metrik | Ohne Stack | Mit Stack | Verbesserung |
Einfache Q&A-Kosten | Voller Preis | $0 (Chat-Modus) | 100 % gespart |
10-URL-Recherche | 150+ Sekunden | 1,3 Sekunden | 115x schneller |
Web-Scraping-Aufgabe | Volle Kredite | 33 % des Originals | 67 % gespart |
Full-Stack-Web-App | Volle Kredite | 40 % des Originals | 60 % gespart |
Recherchebericht | 12 Minuten | 2 Minuten | 6x schneller |
Durchschnitt über alle Aufgaben | Basislinie | 47 % weniger | 47 % gespart |
Der Power Stack kostet einmalig $9 (oder $12 für das Bundle mit Fast Navigation). So schnell zahlt er sich aus:
Plan | Monatliche Kosten | 47 % Verschwendung | Tägliche Verschwendung | Amortisation |
Plus ($39/Monat) | 6.500 Kr./Tag | ~3.055 Kr./Tag | ~$0,44/Tag | ~20 Prompts |
Max ($99/Monat) | 16.250 Kr./Tag | ~7.637 Kr./Tag | ~$0,92/Tag | ~10 Prompts |
Teams ($79/Benutzer/Monat) | Variabel | ~47 % | Variabel | 1–2 Tage |
Nach der Amortisation spart Ihnen jeder Prompt Geld. Für den Rest Ihres Manus-Abonnements. Für immer.
Geschätzte jährliche Ersparnis: ~$500–$1.000+ je nach Nutzung.
Kopieren Sie die Skill-Dateien nach ~/skills/credit-optimizer/ und ~/skills/fast-navigation/
Beide werden bei jeder Aufgabe automatisch aktiviert — keine Konfiguration erforderlich
pip install mcp-credit-optimizer
python -m mcp_credit_optimizerFügen Sie dies zu Ihrer MCP-Konfiguration hinzu:
{
"mcpServers": {
"credit-optimizer": {
"command": "python",
"args": ["-m", "mcp_credit_optimizer"]
}
}
}Funktioniert mit Claude Desktop, Cursor, Windsurf, Copilot und jedem MCP-kompatiblen Client.
Warum bezahlen, wenn der MCP-Server kostenlos ist? Der MCP-Server spart Kredite, wenn Sie daran denken, ihn aufzurufen. Der Manus Skill spart Kredite bei jedem einzelnen Prompt automatisch — kein manueller Aufruf erforderlich. Der Skill enthält auch Fast Navigation (115-facher Geschwindigkeitsvorteil), was als MCP nicht verfügbar ist.
Alle 53 Testszenarien bestehen mit null Qualitätsverlust:
Kategorie | Szenarien | Qualitätsverlust |
Code-Generierung (Python, JS, React, SQL) | 12 | 0 % |
Kreatives Schreiben (Blog, Marketing) | 8 | 0 % |
Datenanalyse (CSV, JSON, API) | 7 | 0 % |
Recherche (Multi-Quellen-Synthese) | 6 | 0 % |
Übersetzung & Lokalisierung | 5 | 0 % |
Fehlerbehebung & Debugging | 5 | 0 % |
Dokumentationserstellung | 5 | 0 % |
Aufgaben mit gemischter Absicht | 5 | 0 % |
"Jeder hasst das kreditbasierte System von Manus. Es ist wahnsinnig, wie schnell Kredite verbrannt werden." — Reddit, 90 Upvotes
"Ich habe in anderthalb Wochen 100.000 Kredite verbraucht. Das ist mein gesamtes monatliches Kontingent." — Reddit
"Manus ist so teuer, dass es absurd ist. $200/Monat und die Kredite gehen trotzdem aus." — Reddit
Der Power Stack existiert, weil diese Frustrationen real sind — und behebbar.
Awesome Manus AI — Kuratierte Liste von Manus AI-Ressourcen, Tools und Tipps
Preisguide 2026 — Vollständige Aufschlüsselung der Manus AI-Preise
Standard vs. Max Guide — Wann welcher Modus zu verwenden ist
Tutorial — Vollständiger Anfängerleitfaden
Glossar — 30+ Manus AI-Begriffe erklärt
Roadmap — Was als Nächstes kommt
Kanal | URL |
Landing Page | |
Einzelkauf ($9) | |
Bundle-Kauf ($12) | |
SkillFlow | |
PyPI (Kostenloses MCP) | |
Smithery | |
PulseMCP | |
Awesome Liste |
MIT — siehe LICENSE für Details.
Erstellt von 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.
