MCP MoP Uploader

Purpose

This application is built to automate the generation and storage of Method of Procedure (MoP) documents using a Large Language Model (LLM) from OpenAI, integrated with AWS S3 via a custom [Model Context Protocol (MCP)] tool.
The goal is to reduce manual effort in creating MoP documents and streamline their delivery to cloud storage systems.

Setup Instructions

1. Clone the repository

git clone https://github.com/adhika28/MCP-AWS-S3Upload.git
cd MCP-AWS-S3Upload

2. Create a virtual environment

python3 -m venv .venv
source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Set environment variables

You can set them via terminal or create a .env file:

export OPENAI_API_KEY="your-openai-api-key"
export AWS_ACCESS_KEY_ID="your-aws-access-key-id"
export AWS_SECRET_ACCESS_KEY="your-aws-secret-key"
export S3_BUCKET="your-s3-bucket-name"

Usage

Run the main script:

python run_mop.py

You will be prompted to enter a scenario:

MoP apa yang ingin kamu buat? provisioning EC2 with Amazon Linux

The app will:

1. Generate a MoP document using OpenAI's GPT-4.
2. Save it locally as Generated_MoP.docx.
3. Upload the file to s3://test-mcp-bucket/MoP/Generated_MoP.docx.

Project Structure

.
├── run_mop.py                # Main application
├── requirements.txt          # Python dependencies
├── mcp/tools/base.py         # Simulated MCP toolbase
└── examples/tools/s3_uploader.py  # Custom S3 uploader tool

imitations and Known Issues

• No input validation or prompt quality check is implemented.
• Document approval workflow or versioning is not included.
• S3 permissions must be correctly configured; otherwise, upload will fail with AccessDenied.

Future Improvements

• Add web UI for user input
• Implement version tracking and metadata tagging for MoP files
• Add approval flow

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