MCP Server Research Project: Scalable AI & Code-Driven Innovation

Pioneering AI breakthroughs with MCP servers & Claude code research—optimized for scalable, code-driven innovation.

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About MCP Server Research Project

What is MCP Server Research Project: Scalable AI & Code-Driven Innovation?

The MCP Server Research Project focuses on implementing the Message Coordination Protocol (MCP), a standardized communication framework for distributed systems. This project provides a reference server implementation, demos, and tools to enable scalable message coordination, with an emphasis on integrating AI-driven workflows and code-first development practices. The repository includes everything needed to explore MCP's capabilities, from server setup to real-time data routing.

How to Use MCP Server Research Project: Scalable AI & Code-Driven Innovation?

Getting started is straightforward:

Clone the repository: git clone https://github.com/ckz/mcp_server_research.git
Create a Python virtual environment and install dependencies
Run the server with default settings or customize via environment variables like DEBUG and PORT
Launch the client demo to test message exchanges with the server

Full instructions and examples are included in the /docs folder.

MCP Server Research Project Features

Key Features

Real-time client registration and message routing across distributed nodes
Support for JSON/YAML message formats with built-in validation
Interactive web dashboard for monitoring message traffic and system health
Pluggable architecture for custom message handlers and AI processing pipelines
Production-ready error handling with automatic retry mechanisms

Use Cases

Coordinating microservices in AI-driven applications
Real-time data synchronization between frontend and backend systems
Orchestrating distributed machine learning training workflows
Building IoT device communication hubs with message prioritization
Testing message brokers for high-frequency trading systems

MCP Server Research Project FAQ

FAQ

Q: What Python versions are supported?
A: Requires Python 3.8+ for type hints and async/await compatibility

Q: Can I extend the protocol?
A: Yes - the plugin system allows adding new message types and processing logic

Q: How is performance optimized?
A: Uses zero-copy message passing and connection pooling for low-latency

Q: How do I contribute?
A: Submit PRs via GitHub and follow the contribution guidelines in contributing.md

Content

MCP Server Research Project

This project contains research and demos related to MCP (Message Coordination Protocol) server implementation.

Overview

The Message Coordination Protocol (MCP) provides a standardized way for distributed systems to communicate and coordinate actions. This repository includes a reference implementation and demonstrations of the protocol in action.

Project Structure

mcp_server_research/
├── docs/          # Documentation files
├── src/           # Source code
│   └── demo/      # Demo implementations
└── requirements.txt

Getting Started

Prerequisites

Python 3.8+
Flask
Requests

Installation

# Clone the repository
git clone https://github.com/ckz/mcp_server_research.git
cd mcp_server_research

# Set up virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running the Demo

To run the MCP server with default settings:

cd src/demo
python simple_mcp_server.py

To run with custom settings using environment variables:

cd src/demo
DEBUG=true PORT=5001 python simple_mcp_server.py

Then visit http://localhost:5001 (or the custom port you specified) to view the dashboard.

To run a client demo in a separate terminal:

cd src/demo
python client_demo.py --server http://localhost:5001 --duration 30 --interval 2

You should see the server accepting connections and the client successfully sending messages.

Demo Features

The simple MCP server demo implements a basic message coordination protocol server that:

Registers and tracks connected clients
Processes various message types (heartbeat, data, command)
Routes messages to appropriate destinations
Provides a web dashboard for monitoring system activity
Maintains an in-memory message history

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Changelog

2024-02-24

Added proper error handling to the client code for connection failures
Using sessions in the client for better connection management
Added CORS support to the server for cross-origin requests
Implemented timeouts for API calls to prevent hanging
Made debug mode configurable through environment variables
Fixed dependency issues by adding flask-cors and specifying werkzeug version
Improved documentation in code comments