Praneeths MCP Server

🚀 Model Context Protocol (MCP) Server

The Model Context Protocol (MCP) Server is a flexible and modular server designed to handle a variety of specialized context-aware tasks. The current implementation includes MCP modules for research and web scraping, making it an ideal foundation for projects that require automated research or real-time web data extraction.

🚀 Quick Start

Installation

Follow these simple steps to set up the MCP Server locally:

1. Clone the repository:

git clone https://github.com/praneethk-ai/praneeths_mcp_server.git
cd MCP_Server

2. Set up and activate a Python virtual environment:

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

3. Install the dependencies:

pip install -r requirements.txt

4. Run the server:

python server.py

Usage

• server.py: The main script to start the MCP Server.
• Interact with the server via the command line, select the desired module, and provide input.

Command Examples:

# Start the server
python server.py

# Run the research module
python server.py research

# Execute a web scraping task
python server.py scraping

✨ Features

Modular Design 🔗

• Easy integration of new MCP modules.
• Each MCP module runs independently but integrates seamlessly with the central server.

Dedicated MCP Modules 🌐

• ResearchMCP: Provides real-time insights and summarized content for user-specified research queries. Adopts a structured approach to verify and present results from trusted sources.
• WebscrapingMCP: Automates web data collection, quickly and efficiently extracting structured content from specified websites.

Logging and Debugging 📋

• Built-in comprehensive logging functionality to track system events, enable effective debugging, and monitor MCP interactions.

User-Friendly Interaction 🤖

• Interactive prompts in the terminal interface for dynamic switching of MCP modes.
• Clear and detailed responses to user requests, enhancing usability and readability.

📚 Documentation

Overview 📚

In today's world, real-time data and reliable research are the foundation for making informed decisions. This project provides a centralized and scalable platform to easily integrate specialized functions such as:

• Automated Research: Quickly obtain summarized research results for the latest developments and historical backgrounds.
• Web Scraping: Seamlessly scrape and process web data, transforming it into structured and meaningful information.

The MCP Server offers flexibility, clarity, and scalability, allowing developers and researchers to easily integrate and expand new MCP modules according to project requirements.

Purpose and Motivation 🎯

The MCP Server provides a centralized and scalable platform that enables easy integration of specialized functions, meeting the needs of real-time data and reliable research in decision-making.

Technology Stack 🛠️

• Python: Core implementation and logic.
• Argparse: Simplifies command-line parameter handling.
• Logging: For detailed logs and debugging information.
• JSON: Structured data processing and communication.

Future Plans 🌟

This project aims to continuously expand by adding more specialized MCP modules, such as:

• AI-driven analysis
• Real-time data processing
• Natural language generation
• Sentiment and trend analysis

The modular design encourages innovation and the integration of new features.

Contribution 🤝

Contributions, enhancements, or feature requests are welcome! Please open an issue or a pull request to start collaborating.

License 📝

This project is under the MIT License. For more information, please see the LICENSE file.