MCP API Automation

MCP-API-Automation Overview

What is MCP-API-Automation?

MCP-API-Automation is a powerful server application developed by limpidnetwork. It is designed to streamline the process of authenticating and downloading data from BP MCP 18x environments. By leveraging the Model Context Protocol (MCP), this server allows AI systems to securely connect to various data sources, facilitating seamless access to real-time information. MCP-API-Automation serves as a reliable tool for organizations seeking to integrate AI capabilities with their existing data infrastructure through a client-server architecture.

How to Use MCP-API-Automation

Using MCP-API-Automation involves setting up a stand-alone container for seamless data interaction. Here's a step-by-step guide:

1. Start the Container:

   • Build and run the Docker container using the following commands:

     docker-compose build
     docker-compose up -d
     

   • These commands will initiate the container, allowing it to authenticate and download necessary data.

2. Set Up Lab Credentials:

   • Specify your lab credentials within the docker-compose.yml file. These credentials are made available as environment variables within the Docker environment.
   • Ensure a lab is scheduled through the appropriate portal to obtain active credentials.

3. Deployment on Windows:

   • Install Python 3.7 to get started.
   • Clone the necessary repository to your local machine.
   • Install the required dependencies using:

     pip install -r requirements.txt
     

   • Configure environment variables for your lab and database settings, such as LAB_USERNAME, LAB_PASSWORD, LAB_ID, POSTGRES_HOST, POSTGRES_USERNAME, POSTGRES_PASSWORD, and POSTGRES_DB.

4. Run the Program:

   • Environment variables can be set directly or passed as parameters to the Python program for flexibility.

Key Features of MCP-API-Automation

• Secure Data Access: Enables secure interaction with data sources, ensuring sensitive information is protected while being accessible to integrated AI systems.

• Flexible Deployment: Offers deployment options on different systems, including Windows, enhancing accessibility for various user environments.

• Environment Variable Configuration: Allows for easy setup and customization through environment variables, providing flexibility in user-specific configurations.

• Real-Time Data Integration: Facilitates the integration of real-time data into AI systems, aiding in the development of responsive and intelligent applications.

• Support for AI Protocols: Utilizes the Model Context Protocol (MCP) to provide a standardized approach for data access and tool integration, promoting interoperability among AI platforms.

MCP-API-Automation serves as an essential tool for businesses and developers looking to harness the power of AI by providing a secure and efficient means of connecting AI systems with their existing data infrastructure.