Scaling Document Trust: MCP in Distributed Server Architectures
Intro
Imagine having to verify sensitive documents across a distributed system—fast, securely, and intelligently. With data authenticity under constant scrutiny and manual workflows creating bottlenecks, organizations must find new ways to streamline document trust and understanding. This is where the Model Context Protocol (MCP) SDK comes in—not just as a framework, but as a launchpad to unlock the AI future of document workflows
Why it matters ?
Whether it’s legal contracts, financial reports, or internal communications, the need for instant verification and insightful summarization is universal. Traditional systems often fall short in flexibility, scalability, and privacy. MCP SDK tackles these challenges head-on with:
- End-to-end encrypted verification workflow
- Modular architecture across clients and servers
- Azure OpenAI integration for AI-powered summarization
What we are building
In this walk-through, we’ll explore how to set up a secure document verification and summarization system using MCP SDK. This hands-on demo includes:
- Verify document status in real time (e.g., verified, revoked)
- Generate AI-powered summaries using Azure OpenAI
- Ensure secure, end-to-end encrypted communication between clients and servers
Welcome to the future of intelligent document pipelines! Let’s unlock the AI future of modern and secure development!
Quick Setup Guide
Key Features That Unlock the AI Future
Each component—client, server, summarizer—can be run independently or together. That means total control and flexible deployment.
To make document processing even more intuitive, the MCP SDK ecosystem now includes a modern Web UI—bridging power and usability in one elegant interface. Built with responsive design principles , this Flask-based frontend allows users to verify documents and generate AI-powered summaries with just a few clicks. Real-time activity logs and status indicators give immediate feedback, transforming your workflow into a visually guided, interactive experience.
Whether you’re processing documents in bulk or validating a single access code, the Web UI supports multiple modes: verify-only, summarize-only, or full pipeline processing. This interface isn’t just cosmetic—it redefines interaction by streamlining access to the backend services while maintaining the same secure hybrid encryption model. You’re not just clicking buttons; you’re orchestrating a decentralized intelligence hub. All of it—encrypted, extensible, and ready to unlock the AI future.
The components:
Web UI (`web_ui/`): Modern Flask-based interface
Simple Clients (`clients/`): Direct MCP function access
MCP Servers(`servers/`): Document and summarization services
Shared Utils (`shared/`): Encryption and configuration
The following script is a fast start
Screenshots
Technical Aspects
Looking into the repo someone may find a lot of information that will really make your life easier! For example here is the script for Windows prepared to do the setup:
@echo off
REM Setup script for MCP SDK Implementation (Windows)
echo Setting up MCP SDK Implementation...
REM Create virtual environment
echo Creating virtual environment...
python -m venv mcp_env
REM Activate virtual environment
echo Activating virtual environment...
call mcp_env\Scripts\activate
REM Install requirements
echo Installing Python packages...
pip install -r requirements.txt
REM Create keys directory
echo Creating keys directory...
mkdir keys\document_server 2>nul
mkdir keys\summarization_server 2>nul
mkdir keys\document_client 2>nul
mkdir keys\summarization_client 2>nul
REM Copy environment template
echo Setting up environment variables...
if not exist .env (
copy .env.example .env
echo Created .env file from template. Please update with your Azure OpenAI credentials.
)
echo.
echo Setup complete!
echo.
echo Next steps:
echo 1. Update .env file with your Azure OpenAI credentials
echo 2. Activate the virtual environment:
echo call mcp_env\Scripts\activate
echo 3. Start the servers:
echo python servers\document_verification_server\server.py
echo python servers\summarization_server\server.py
echo 4. Run the clients:
echo python clients\document_client.py
echo python clients\summarization_client.py
pause
You will also find ready scripts, prepared for quick look into the demo without the need of the Web UI.
Lessons Learned from the Demo
- Modularity Wins: Running separate servers and clients helps isolate issues and scale securely.
- Minimal Setup, Maximum Impact: The SDK’s setup time is impressively short, making it ideal for POCs or rapid prototyping.
- AI Integration Is Optional But Powerful: Even without Azure OpenAI, document verification is robust and secure. But turning on summarization truly lets you unlock the AI future.
To-Do (Coming Up)
Once your MCP-based document exchange system is up and running locally, it’s time to move toward a production-ready deployment in Azure. This unlocks enterprise-grade scalability, security, and reliability. Start by containerizing the services (Flask Web UI, MCP clients, and servers) using Docker, then deploy to Azure services like Azure App Service or Azure Container Apps for flexible orchestration. Store API keys and credentials in Azure Key Vault, and access them securely without hardcoding secrets. For authentication, leverage Azure Managed Identities to control access to Key Vault and other Azure resources—no credentials needed, just identity-aware security.
Closing Thoughts
The MCP SDK isn’t just a tool—it’s a mindset shift. It empowers developers and organizations to embrace intelligent, decentralized, and secure documentation workflows, with AI as a natural companion. With just a few commands, you unlock an architecture that’s ready for the future.
References:
- Get Started with MCP
- The Azure MCP Server
- MCP Servers in VSCode
- MCP Python SDK
- CloudBlogger: Contextual MCP – The idea
