MCP Usecase

🚀 Quick Start Auto MCP: All-in-one Tool for Claude Desktop and Cursor

Quick-start Auto MCP is a tool that helps you quickly and easily register Anthropic's Model Context Protocol (MCP) in Claude Desktop and Cursor.

Key Advantages:

1. Rapid Setup: Simply run the tool and copy-paste the generated JSON file to effortlessly add MCP functionality to Claude Desktop and Cursor.
2. Continuous Updates of Various Tools: We are constantly updating a variety of useful MCP tools. Stay up - to - date with your toolbox by following and starring us.

📚 Table of Contents

• Features
• Project Structure
• Requirements
• Installation
• Configuration
• Usage
• Troubleshooting
• License
• Contribution
• Contact Us
• Author

✨ Features

• RAG (Retrieval Augmented Generation) - Keyword, semantic, and hybrid search functions for PDF documents.
• Dify External Knowledge API - Conduct document searches via Dify's external knowledge API.
• Dify Workflow - Execute and obtain the results of Dify workflows.
• Web Search - Implement real - time web search using the Tavily API.
• Automatic JSON Generation - Automatically generate MCP JSON files for Claude Desktop and Cursor.

📁 Project Structure

.
├── case1                     # RAG example
├── case2                     # Dify external knowledge API example
├── case3                     # Dify workflow example
├── case4                     # Web search example
├── data                      # Example data files
├── docs                      # Documentation directory
│   ├── case1.md           # case1 description 🚨 Contains tips for optimizing tool calls
│   ├── case2.md           # case2 description
│   ├── case3.md           # case3 description
│   ├── case4.md           # case4 description
│   └── installation.md    # Installation guide
├── .env.example              # .env example format
├── pyproject.toml            # Project settings
├── requirements.txt          # List of required packages
└── uv.lock                   # uv lock file

📋 Requirements

• Python >= 3.11
• Claude Desktop or Cursor (version supporting MCP)
• uv (recommended) or pip

📦 Installation

Steps:

1. Clone the repository:

   git clone [repository URL]
   cd [project name]
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Configure environment variables:

   • Create a .env file and add the following content:

     API_KEY=your_api_key
     DATABASE_URL=your_database_url
     

4. Run the project:

   uvicorn main:app --reload
   

⚙️ Configuration

• Edit the pyproject.toml file to configure project dependencies and settings.

💻 Usage

1. Clone the repository and install dependencies:

git clone [repository URL]
cd [project name]
pip install -r requirements.txt

2. Configure environment variables:

• Create a .env file:

  API_KEY=your_api_key
  DATABASE_URL=your_database_url
  

3. Run the project:

uvicorn main:app --reload

🛠️ Troubleshooting

• API errors: Check if the environment variables are correctly set.
• Virtual environment issues: Ensure that the Python version is 3.11 or higher.

📄 License

MIT LICENSE

🤝 Contribution

All contributions are welcome! Participate in the project by reporting issues or submitting pull requests.

📞 Contact Us

If you need help or have any questions, please contact us at: dev@brain - crew.com

👨‍💻 Author

Hantaek Lim