Manus Credit Optimizer

Manus Credit Optimizer

An MCP server that reduces Manus AI credit usage by up to 75% through intelligent prompt compression, smart model routing, and intent classification. It provides tools to analyze and optimize prompts for maximum efficiency without sacrificing quality.

Category
访问服务器

README

🚀 Credit Optimizer v5 for Manus AI

Cut your Manus AI credit usage by 30-75% with ZERO quality loss.

Audited across 53 real-world scenarios. Works as an MCP Server (Claude, Cursor, Copilot, Codex, Windsurf, Cline) or as a native Manus Skill.

License: MIT Python 3.10+ MCP Compatible


📊 Results

Metric Before After Improvement
Average credits per task 100% 53% 47% reduction
Quality score 9.2/10 9.2/10 0% loss
Tasks audited - 200+ Verified
Scenarios tested - 53 All passing

🎯 What It Does

Credit Optimizer analyzes your prompts and tasks before execution, then applies intelligent optimization strategies:

  • Smart Model Routing — Routes simple tasks to Standard mode, complex tasks to Max mode
  • Intent Classification — Detects 12 task categories (code_fix, creative_writing, data_analysis, etc.)
  • Prompt Compression — Removes redundancy while preserving all semantic meaning
  • Batch Detection — Identifies tasks that can be parallelized for fewer credits
  • Context Hygiene — Flags unnecessary context that inflates token usage
  • Output Format Optimization — Suggests efficient output formats (file vs inline)

⚡ Quick Start

As MCP Server (Claude Desktop, Cursor, etc.)

# Install
pip install fastmcp

# Clone this repo
git clone https://github.com/rafsilva85/manus-credit-optimizer.git
cd manus-credit-optimizer

# Run the server
python -m mcp_credit_optimizer

Add to your MCP client config (e.g., Claude Desktop claude_desktop_config.json):

{
  "mcpServers": {
    "credit-optimizer": {
      "command": "python",
      "args": ["-m", "mcp_credit_optimizer"],
      "cwd": "/path/to/manus-credit-optimizer"
    }
  }
}

As Manus Skill (Native Integration)

  1. Purchase the full Manus Skill at creditopt.ai
  2. Install as a Manus Skill following the included instructions
  3. The skill auto-activates on every task — no manual intervention needed

🔧 MCP Tools Available

Tool Description
analyze_prompt Analyze a prompt and get optimization recommendations with estimated savings
optimize_prompt Get an optimized version of your prompt ready to use
estimate_savings Quick estimate of potential credit savings for a task description

📋 Example Usage

You: analyze_prompt("Build me a React dashboard with charts, authentication, and a database backend")

Credit Optimizer: 
  ✅ Intent: code_generation (complex)
  ✅ Recommended: Max mode (complex multi-component task)
  ✅ Optimization: Split into 3 sequential tasks
     1. Database schema + API routes
     2. Authentication flow  
     3. React dashboard + charts
  ✅ Estimated savings: 35-45%
  ✅ Quality impact: None

🏗️ Architecture

Prompt Input → Intent Classifier → Complexity Scorer → Strategy Selector
                                                            ↓
                                              ┌─────────────────────────┐
                                              │ • Model Router          │
                                              │ • Prompt Compressor     │
                                              │ • Batch Detector        │
                                              │ • Context Hygiene       │
                                              │ • Output Optimizer      │
                                              └─────────────────────────┘
                                                            ↓
                                              Optimized Recommendations

🔒 Audit Results

All 53 test scenarios pass with zero quality degradation:

  • ✅ Code generation (Python, JS, React, SQL)
  • ✅ Creative writing (blog posts, marketing copy)
  • ✅ Data analysis (CSV, JSON, API data)
  • ✅ Research tasks (multi-source synthesis)
  • ✅ Translation & localization
  • ✅ Bug fixing & debugging
  • ✅ Documentation generation
  • ✅ Mixed-intent tasks

💰 Pricing

Option Price What You Get
MCP Server Free (this repo) MCP tools for any MCP client
Manus Skill $29 one-time Native Manus integration + auto-activation + priority updates

👉 Get the full Manus Skill at creditopt.ai

📄 License

MIT License — use it freely in personal and commercial projects.

🤝 Contributing

Issues and PRs welcome! If you find a scenario where the optimizer reduces quality, please open an issue with the prompt and expected output.


Built by Rafael Silva | creditopt.ai

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选