Lessons from Early Adoption

🌟 Lessons from Early Adopters

🎯 What This Module Covers

This module explores how real organizations and developers are leveraging the Model Context Protocol (MCP) to solve actual challenges and drive innovation.

Through detailed case studies, hands-on projects, and practical examples, you'll discover how MCP enables secure, scalable AI integration that connects language models, tools, and enterprise data.

📚 See MCP in Action

Want to see these principles applied to production-ready tools?

Check out our 10 Microsoft MCP Servers That Are Transforming Developer Productivity, which showcases real Microsoft MCP servers you can use today.

Overview

This lesson explores how early adopters have leveraged the Model Context Protocol (MCP) to solve real-world challenges and drive innovation across industries.

Through detailed case studies and hands-on projects, you'll see how MCP enables standardized, secure, and scalable AI integration—connecting large language models, tools, and enterprise data in a unified framework.

You'll gain practical experience designing and building MCP-based solutions, learn from proven implementation patterns, and discover best practices for deploying MCP in production environments.

The lesson also highlights emerging trends, future directions, and open-source resources to help you stay at the forefront of MCP technology and its evolving ecosystem.

Learning Objectives

Analyze real-world MCP implementations across different industries

Design and build complete MCP-based applications

Explore emerging trends and future directions in MCP technology

Apply best practices in actual development scenarios

Real-world MCP Implementations

Case Study 1: Enterprise Customer Support Automation

A multinational corporation implemented an MCP-based solution to standardize AI interactions across their customer support systems. This allowed them to:

Create a unified interface for multiple LLM providers

Maintain consistent prompt management across departments

Implement robust security and compliance controls

Easily switch between different AI models based on specific needs

Technical Implementation:

# Python MCP server implementation for customer support

import logging

import asyncio

from modelcontextprotocol import create_server, ServerConfig

from modelcontextprotocol.server import MCPServer

from modelcontextprotocol.transports import create_http_transport

from modelcontextprotocol.resources import ResourceDefinition

from modelcontextprotocol.prompts import PromptDefinition

from modelcontextprotocol.tool import ToolDefinition



# Configure logging

logging.basicConfig(level=logging.INFO)



async def main():

    # Create server configuration

    config = ServerConfig(

        name="Enterprise Customer Support Server",

        version="1.0.0",

        description="MCP server for handling customer support inquiries"

    )

    

    # Initialize MCP server

    server = create_server(config)

    

    # Register knowledge base resources

    server.resources.register(

        ResourceDefinition(

            name="customer_kb",

            description="Customer knowledge base documentation"

        ),

        lambda params: get_customer_documentation(params)

    )

    

    # Register prompt templates

    server.prompts.register(

        PromptDefinition(

            name="support_template",

            description="Templates for customer support responses"

        ),

        lambda params: get_support_templates(params)

    )

    

    # Register support tools

    server.tools.register(

        ToolDefinition(

            name="ticketing",

            description="Create and update support tickets"

        ),

        handle_ticketing_operations

    )

    

    # Start server with HTTP transport

    transport = create_http_transport(port=8080)

    await server.run(transport)



if __name__ == "__main__":

    asyncio.run(main())

Results: 30% reduction in model costs, 45% improvement in response consistency, and enhanced compliance across global operations.

Case Study 2: Healthcare Diagnostic Assistant

A healthcare provider developed an MCP infrastructure to integrate multiple specialized medical AI models while ensuring sensitive patient data remained protected:

Seamless switching between generalist and specialist medical models

Strict privacy controls and audit trails

Integration with existing Electronic Health Record (EHR) systems

Consistent prompt engineering for medical terminology

Technical Implementation:

// C# MCP host application implementation in healthcare application

using Microsoft.Extensions.DependencyInjection;

using ModelContextProtocol.SDK.Client;

using ModelContextProtocol.SDK.Security;

using ModelContextProtocol.SDK.Resources;



public class DiagnosticAssistant

{

    private readonly MCPHostClient _mcpClient;

    private readonly PatientContext _patientContext;

    

    public DiagnosticAssistant(PatientContext patientContext)

    {

        _patientContext = patientContext;

        

        // Configure MCP client with healthcare-specific settings

        var clientOptions = new ClientOptions

        {

            Name = "Healthcare Diagnostic Assistant",

            Version = "1.0.0",

            Security = new SecurityOptions

            {

                Encryption = EncryptionLevel.Medical,

                AuditEnabled = true

            }

        };

        

        _mcpClient = new MCPHostClientBuilder()

            .WithOptions(clientOptions)

            .WithTransport(new HttpTransport("https://healthcare-mcp.example.org"))

            .WithAuthentication(new HIPAACompliantAuthProvider())

            .Build();

    }

    

    public async Task<DiagnosticSuggestion> GetDiagnosticAssistance(

        string symptoms, string patientHistory)

    {

        // Create request with appropriate resources and tool access

        var resourceRequest = new ResourceRequest

        {

            Name = "patient_records",

            Parameters = new Dictionary<string, object>

            {

                ["patientId"] = _patientContext.PatientId,

                ["requestingProvider"] = _patientContext.ProviderId

            }

        };

        

        // Request diagnostic assistance using appropriate prompt

        var response = await _mcpClient.SendPromptRequestAsync(

            promptName: "diagnostic_assistance",

            parameters: new Dictionary<string, object>

            {

                ["symptoms"] = symptoms,

                patientHistory = patientHistory,

                relevantGuidelines = _patientContext.GetRelevantGuidelines()

            });

            

        return DiagnosticSuggestion.FromMCPResponse(response);

    }

}

Results: Improved diagnostic suggestions for physicians while maintaining full HIPAA compliance and significant reduction in context-switching between systems.

Case Study 3: Financial Services Risk Analysis

A financial institution implemented MCP to standardize their risk analysis processes across different departments:

Created a unified interface for credit risk, fraud detection, and investment risk models

Implemented strict access controls and model versioning

Ensured auditability of all AI recommendations

Maintained consistent data formatting across diverse systems

Technical Implementation:

// Java MCP server for financial risk assessment

import org.mcp.server.*;

import org.mcp.security.*;



public class FinancialRiskMCPServer {

    public static void main(String[] args) {

        // Create MCP server with financial compliance features

        MCPServer server = new MCPServerBuilder()

            .withModelProviders(

                new ModelProvider("risk-assessment-primary", new AzureOpenAIProvider()),

                new ModelProvider("risk-assessment-audit", new LocalLlamaProvider())

            )

            .withPromptTemplateDirectory("./compliance/templates")

            .withAccessControls(new SOCCompliantAccessControl())

            .withDataEncryption(EncryptionStandard.FINANCIAL_GRADE)

            .withVersionControl(true)

            .withAuditLogging(new DatabaseAuditLogger())

            .build();

            

        server.addRequestValidator(new FinancialDataValidator());

        server.addResponseFilter(new PII_RedactionFilter());

        

        server.start(9000);

        

        System.out.println("Financial Risk MCP Server running on port 9000");

    }

}

Results: Enhanced regulatory compliance, 40% faster model deployment cycles, and improved risk assessment consistency across departments.

Case Study 4: Microsoft Playwright MCP Server for Browser Automation

Microsoft developed the Playwright MCP server to enable secure, standardized browser automation through the Model Context Protocol.

This production-ready server allows AI agents and LLMs to interact with web browsers in a controlled, auditable, and extensible way—enabling use cases such as automated web testing, data extraction, and end-to-end workflows.

> 🎯 Production Ready Tool

>

> This case study showcases a real MCP server you can use today!

Learn more about the Playwright MCP Server and 9 other production-ready Microsoft MCP servers in our Microsoft MCP Servers Guide.

Key Features:

Exposes browser automation capabilities (navigation, form filling, screenshot capture, etc.) as MCP tools

Implements strict access controls and sandboxing to prevent unauthorized actions

Provides detailed audit logs for all browser interactions

Supports integration with Azure OpenAI and other LLM providers for agent-driven automation

Powers GitHub Copilot's Coding Agent with web browsing capabilities

Technical Implementation:

// TypeScript: Registering Playwright browser automation tools in an MCP server

import { createServer, ToolDefinition } from 'modelcontextprotocol';

import { launch } from 'playwright';



const server = createServer({

  name: 'Playwright MCP Server',

  version: '1.0.0',

  description: 'MCP server for browser automation using Playwright'

});



// Register a tool for navigating to a URL and capturing a screenshot

server.tools.register(

  new ToolDefinition({

    name: 'navigate_and_screenshot',

    description: 'Navigate to a URL and capture a screenshot',

    parameters: {

      url: { type: 'string', description: 'The URL to visit' }

    }

  }),

  async ({ url }) => {

    const browser = await launch();

    const page = await browser.newPage();

    await page.goto(url);

    const screenshot = await page.screenshot();

    await browser.close();

    return { screenshot };

  }

);



// Start the MCP server

server.listen(8080);

Results:

Enabled secure, programmatic browser automation for AI agents and LLMs

Reduced manual testing effort and improved test coverage for web applications

Provided a reusable, extensible framework for browser-based tool integration in enterprise environments

Powers GitHub Copilot's web browsing capabilities

References:

Playwright MCP Server GitHub Repository

Microsoft AI and Automation Solutions

Case Study 5: Azure MCP – Enterprise-Grade Model Context Protocol as a Service

Azure MCP Server (https://aka.ms/azmcp) is Microsoft’s managed, enterprise-grade implementation of the Model Context Protocol, designed to provide scalable, secure, and compliant MCP server capabilities as a cloud service.

Azure MCP enables organizations to rapidly deploy, manage, and integrate MCP servers with Azure AI, data, and security services, reducing operational overhead and accelerating AI adoption.

> 🎯 Production Ready Tool

>

> This is a real MCP server you can use today! Learn more about the Azure AI Foundry MCP Server in our Microsoft MCP Servers Guide.

Fully managed MCP server hosting with built-in scaling, monitoring, and security

Native integration with Azure OpenAI, Azure AI Search, and other Azure services

Enterprise authentication and authorization via Microsoft Entra ID

Support for custom tools, prompt templates, and resource connectors

Compliance with enterprise security and regulatory requirements

Technical Implementation:

# Example: Azure MCP server deployment configuration (YAML)

apiVersion: mcp.microsoft.com/v1

kind: McpServer

metadata:

  name: enterprise-mcp-server

spec:

  modelProviders:

    - name: azure-openai

      type: AzureOpenAI

      endpoint: https://<your-openai-resource>.openai.azure.com/

      apiKeySecret: <your-azure-keyvault-secret>

  tools:

    - name: document_search

      type: AzureAISearch

      endpoint: https://<your-search-resource>.search.windows.net/

      apiKeySecret: <your-azure-keyvault-secret>

  authentication:

    type: EntraID

    tenantId: <your-tenant-id>

  monitoring:

    enabled: true

    logAnalyticsWorkspace: <your-log-analytics-id>

Results:

Reduced time-to-value for enterprise AI projects by providing a ready-to-use, compliant MCP server platform

Simplified integration of LLMs, tools, and enterprise data sources

Enhanced security, observability, and operational efficiency for MCP workloads

Improved code quality with Azure SDK best practices and current authentication patterns

References:

Azure MCP Documentation

Azure MCP Server GitHub Repository

Azure AI Services

Microsoft MCP Center

Case Study 6: NLWeb

MCP (Model Context Protocol) is an emerging protocol for Chatbots and AI assistants to interact with tools.

Every NLWeb instance is also an MCP server, which supports one core method, ask, which is used to ask a website a question in natural language.

The returned response leverages schema.org, a widely-used vocabulary for describing web data.

Loosely speaking, MCP is NLWeb as Http is to HTML.

NLWeb combines protocols, Schema.org formats, and sample code to help sites rapidly create these endpoints, benefiting both humans through conversational interfaces and machines through natural agent-to-agent interaction.

There are two distinct components to NLWeb.

A protocol, very simple to begin with, to interface with a site in natural language and a format, leveraging json and schema.org for the returned answer. See the documentation on the REST API for more details.

A straightforward implementation of (1) that leverages existing markup, for sites that can be abstracted as lists of items (products, recipes, attractions, reviews, etc.). Together with a set of user interface widgets, sites can easily provide conversational interfaces to their content. See the documentation on Life of a chat query for more details on how this works.

References:

Azure MCP Documentation

NLWeb

Case Study 7: Azure AI Foundry MCP Server – Enterprise AI Agent Integration

Azure AI Foundry MCP servers demonstrate how MCP can be used to orchestrate and manage AI agents and workflows in enterprise environments.

By integrating MCP with Azure AI Foundry, organizations can standardize agent interactions, leverage Foundry's workflow management, and ensure secure, scalable deployments.

> 🎯 Production Ready Tool

>

> This is a real MCP server you can use today!

Learn more about the Azure AI Foundry MCP Server in our Microsoft MCP Servers Guide.

Key Features:

Comprehensive access to Azure's AI ecosystem, including model catalogs and deployment management

Knowledge indexing with Azure AI Search for RAG applications

Evaluation tools for AI model performance and quality assurance

Integration with Azure AI Foundry Catalog and Labs for cutting-edge research models

Agent management and evaluation capabilities for production scenarios

Results:

Rapid prototyping and robust monitoring of AI agent workflows

Seamless integration with Azure AI services for advanced scenarios

Unified interface for building, deploying, and monitoring agent pipelines

Improved security, compliance, and operational efficiency for enterprises

Accelerated AI adoption while maintaining control over complex agent-driven processes

References:

Azure AI Foundry MCP Server GitHub Repository

Integrating Azure AI Agents with MCP (Microsoft Foundry Blog)

Case Study 8: Foundry MCP Playground – Experimentation and Prototyping

The Foundry MCP Playground offers a ready-to-use environment for experimenting with MCP servers and Azure AI Foundry integrations.

Developers can quickly prototype, test, and evaluate AI models and agent workflows using resources from the Azure AI Foundry Catalog and Labs.

The playground streamlines setup, provides sample projects, and supports collaborative development, making it easy to explore best practices and new scenarios with minimal overhead.

It is especially useful for teams looking to validate ideas, share experiments, and accelerate learning without the need for complex infrastructure.

By lowering the barrier to entry, the playground helps foster innovation and community contributions in the MCP and Azure AI Foundry ecosystem.

References:

Foundry MCP Playground GitHub Repository

Case Study 9: Microsoft Learn Docs MCP Server – AI-Powered Documentation Access

The Microsoft Learn Docs MCP Server is a cloud-hosted service that provides AI assistants with real-time access to official Microsoft documentation through the Model Context Protocol.

This production-ready server connects to the comprehensive Microsoft Learn ecosystem and enables semantic search across all official Microsoft sources.

> 🎯 Production Ready Tool

>

> This is a real MCP server you can use today!

Learn more about the Microsoft Learn Docs MCP Server in our Microsoft MCP Servers Guide.

Key Features:

Real-time access to official Microsoft documentation, Azure docs, and Microsoft 365 documentation

Advanced semantic search capabilities that understand context and intent

Always up-to-date information as Microsoft Learn content is published

Comprehensive coverage across Microsoft Learn, Azure documentation, and Microsoft 365 sources

Returns up to 10 high-quality content chunks with article titles and URLs

Why It's Critical:

Solves the "outdated AI knowledge" problem for Microsoft technologies

Ensures AI assistants have access to the latest .NET, C#, Azure, and Microsoft 365 features

Provides authoritative, first-party information for accurate code generation

Essential for developers working with rapidly evolving Microsoft technologies

Results:

Dramatically improved accuracy of AI-generated code for Microsoft technologies

Reduced time spent searching for current documentation and best practices

Enhanced developer productivity with context-aware documentation retrieval

Seamless integration with development workflows without leaving the IDE

References:

Microsoft Learn Docs MCP Server GitHub Repository

Microsoft Learn Documentation

Hands-on Projects

Project 1: Build a Multi-Provider MCP Server

Objective: Create an MCP server that can route requests to multiple AI model providers based on specific criteria.

Requirements:

Support at least three different model providers (e.g., OpenAI, Anthropic, local models)

Implement a routing mechanism based on request metadata

Create a configuration system for managing provider credentials

Add caching to optimize performance and costs

Build a simple dashboard for monitoring usage

Implementation Steps:

1. Set up the basic MCP server infrastructure

2. Implement provider adapters for each AI model service

3. Create the routing logic based on request attributes

4. Add caching mechanisms for frequent requests

5. Develop the monitoring dashboard

6. Test with various request patterns

Technologies: Choose from Python (.NET/Java/Python based on your preference), Redis for caching, and a simple web framework for the dashboard.

Project 2: Enterprise Prompt Management System

Objective: Develop an MCP-based system for managing, versioning, and deploying prompt templates across an organization.

Requirements:

Create a centralized repository for prompt templates

Implement versioning and approval workflows

Build template testing capabilities with sample inputs

Develop role-based access controls

Create an API for template retrieval and deployment

Implementation Steps:

1. Design the database schema for template storage

2. Create the core API for template CRUD operations

3. Implement the versioning system

4. Build the approval workflow

5. Develop the testing framework

6. Create a simple web interface for management

7. Integrate with an MCP server

Technologies: Your choice of backend framework, SQL or NoSQL database, and a frontend framework for the management interface.

Project 3: MCP-Based Content Generation Platform

Objective: Build a content generation platform that leverages MCP to provide consistent results across different content types.

Requirements:

Support multiple content formats (blog posts, social media, marketing copy)

Implement template-based generation with customization options

Create a content review and feedback system

Track content performance metrics

Support content versioning and iteration

Implementation Steps:

1. Set up the MCP client infrastructure

2. Create templates for different content types

3. Build the content generation pipeline

4. Implement the review system

5. Develop the metrics tracking system

6. Create a user interface for template management and content generation

Technologies: Your preferred programming language, web framework, and database system.

Future Directions for MCP Technology

Emerging Trends

1. Multi-Modal MCP

- Expansion of MCP to standardize interactions with image, audio, and video models

- Development of cross-modal reasoning capabilities

- Standardized prompt formats for different modalities

2. Federated MCP Infrastructure

- Distributed MCP networks that can share resources across organizations

- Standardized protocols for secure model sharing

- Privacy-preserving computation techniques

3. MCP Marketplaces

- Ecosystems for sharing and monetizing MCP templates and plugins

- Quality assurance and certification processes

- Integration with model marketplaces

4. MCP for Edge Computing

- Adaptation of MCP standards for resource-constrained edge devices

- Optimized protocols for low-bandwidth environments

- Specialized MCP implementations for IoT ecosystems

5. Regulatory Frameworks

- Development of MCP extensions for regulatory compliance

- Standardized audit trails and explainability interfaces

- Integration with emerging AI governance frameworks

MCP Solutions from Microsoft

Microsoft and Azure have developed several open-source repositories to help developers implement MCP in various scenarios:

Microsoft Organization

1. playwright-mcp - A Playwright MCP server for browser automation and testing

2. files-mcp-server - A OneDrive MCP server implementation for local testing and community contribution

3. NLWeb - NLWeb is a collection of open protocols and associated open source tools. Its main focus is establishing a foundational layer for the AI Web

Azure-Samples Organization

1. mcp - Links to samples, tools, and resources for building and integrating MCP servers on Azure using multiple languages

2. mcp-auth-servers - Reference MCP servers demonstrating authentication with the current Model Context Protocol specification

3. remote-mcp-functions - Landing page for Remote MCP Server implementations in Azure Functions with links to language-specific repos

4. remote-mcp-functions-python - Quickstart template for building and deploying custom remote MCP servers using Azure Functions with Python

5. remote-mcp-functions-dotnet - Quickstart template for building and deploying custom remote MCP servers using Azure Functions with .NET/C#

6. remote-mcp-functions-typescript - Quickstart template for building and deploying custom remote MCP servers using Azure Functions with TypeScript

7. remote-mcp-apim-functions-python - Azure API Management as AI Gateway to Remote MCP servers using Python

8. AI-Gateway - APIM ❤️ AI experiments including MCP capabilities, integrating with Azure OpenAI and AI Foundry

These repositories provide various implementations, templates, and resources for working with the Model Context Protocol across different programming languages and Azure services.

They cover a range of use cases from basic server implementations to authentication, cloud deployment, and enterprise integration scenarios.

MCP Resources Directory

The MCP Resources directory in the official Microsoft MCP repository provides a curated collection of sample resources, prompt templates, and tool definitions for use with Model Context Protocol servers.

This directory is designed to help developers quickly get started with MCP by offering reusable building blocks and best-practice examples for:

Prompt Templates: Ready-to-use prompt templates for common AI tasks and scenarios, which can be adapted for your own MCP server implementations.

Tool Definitions: Example tool schemas and metadata to standardize tool integration and invocation across different MCP servers.

Resource Samples: Example resource definitions for connecting to data sources, APIs, and external services within the MCP framework.

Reference Implementations: Practical samples that demonstrate how to structure and organize resources, prompts, and tools in real-world MCP projects.

These resources accelerate development, promote standardization, and help ensure best practices when building and deploying MCP-based solutions.

MCP Resources Directory

MCP Resources (Sample Prompts, Tools, and Resource Definitions)

Research Opportunities

Efficient prompt optimization techniques within MCP frameworks

Security models for multi-tenant MCP deployments

Performance benchmarking across different MCP implementations

Formal verification methods for MCP servers

Conclusion

The Model Context Protocol (MCP) is rapidly shaping the future of standardized, secure, and interoperable AI integration across industries.

Through the case studies and hands-on projects in this lesson, you've seen how early adopters—including Microsoft and Azure—are leveraging MCP to solve real-world challenges, accelerate AI adoption, and ensure compliance, security, and scalability.

MCP's modular approach enables organizations to connect large language models, tools, and enterprise data in a unified, auditable framework.

As MCP continues to evolve, staying engaged with the community, exploring open-source resources, and applying best practices will be key to building robust, future-ready AI solutions.

Additional Resources

MCP Foundry GitHub Repository

Foundry MCP Playground

Integrating Azure AI Agents with MCP (Microsoft Foundry Blog)

MCP GitHub Repository (Microsoft)

MCP Resources Directory (Sample Prompts, Tools, and Resource Definitions)

MCP Community & Documentation

MCP Specification (2025-11-25)

Azure MCP Documentation

OWASP MCP Top 10 - Security best practices

Playwright MCP Server GitHub Repository

Files MCP Server (OneDrive)

Azure-Samples MCP

MCP Auth Servers (Azure-Samples)

Remote MCP Functions (Azure-Samples)

Remote MCP Functions Python (Azure-Samples)

Remote MCP Functions .NET (Azure-Samples)

Remote MCP Functions TypeScript (Azure-Samples)

Remote MCP APIM Functions Python (Azure-Samples)

AI-Gateway (Azure-Samples)

Microsoft AI and Automation Solutions

Exercises

1. Analyze one of the case studies and propose an alternative implementation approach.

2. Choose one of the project ideas and create a detailed technical specification.

3. Research an industry not covered in the case studies and outline how MCP could address its specific challenges.

4. Explore one of the future directions and create a concept for a new MCP extension to support it.

What's Next

Explore more: Microsoft MCP Servers

Continue to: Module 8: Best Practices

# 고객 지원을 위한 Python MCP 서버 구현

import logging

import asyncio

from modelcontextprotocol import create_server, ServerConfig

from modelcontextprotocol.server import MCPServer

from modelcontextprotocol.transports import create_http_transport

from modelcontextprotocol.resources import ResourceDefinition

from modelcontextprotocol.prompts import PromptDefinition

from modelcontextprotocol.tool import ToolDefinition



# 로깅 구성

logging.basicConfig(level=logging.INFO)



async def main():

    # 서버 구성 생성

    config = ServerConfig(

        name="Enterprise Customer Support Server",

        version="1.0.0",

        description="MCP server for handling customer support inquiries"

    )

    

    # MCP 서버 초기화

    server = create_server(config)

    

    # 지식 베이스 리소스 등록

    server.resources.register(

        ResourceDefinition(

            name="customer_kb",

            description="Customer knowledge base documentation"

        ),

        lambda params: get_customer_documentation(params)

    )

    

    # 프롬프트 템플릿 등록

    server.prompts.register(

        PromptDefinition(

            name="support_template",

            description="Templates for customer support responses"

        ),

        lambda params: get_support_templates(params)

    )

    

    # 지원 도구 등록

    server.tools.register(

        ToolDefinition(

            name="ticketing",

            description="Create and update support tickets"

        ),

        handle_ticketing_operations

    )

    

    # HTTP 전송으로 서버 시작

    transport = create_http_transport(port=8080)

    await server.run(transport)



if __name__ == "__main__":

    asyncio.run(main())

// C# MCP host application implementation in healthcare application

using Microsoft.Extensions.DependencyInjection;

using ModelContextProtocol.SDK.Client;

using ModelContextProtocol.SDK.Security;

using ModelContextProtocol.SDK.Resources;



public class DiagnosticAssistant

{

    private readonly MCPHostClient _mcpClient;

    private readonly PatientContext _patientContext;

    

    public DiagnosticAssistant(PatientContext patientContext)

    {

        _patientContext = patientContext;

        

        // Configure MCP client with healthcare-specific settings

        var clientOptions = new ClientOptions

        {

            Name = "Healthcare Diagnostic Assistant",

            Version = "1.0.0",

            Security = new SecurityOptions

            {

                Encryption = EncryptionLevel.Medical,

                AuditEnabled = true

            }

        };

        

        _mcpClient = new MCPHostClientBuilder()

            .WithOptions(clientOptions)

            .WithTransport(new HttpTransport("https://healthcare-mcp.example.org"))

            .WithAuthentication(new HIPAACompliantAuthProvider())

            .Build();

    }

    

    public async Task<DiagnosticSuggestion> GetDiagnosticAssistance(

        string symptoms, string patientHistory)

    {

        // Create request with appropriate resources and tool access

        var resourceRequest = new ResourceRequest

        {

            Name = "patient_records",

            Parameters = new Dictionary<string, object>

            {

                ["patientId"] = _patientContext.PatientId,

                ["requestingProvider"] = _patientContext.ProviderId

            }

        };

        

        // Request diagnostic assistance using appropriate prompt

        var response = await _mcpClient.SendPromptRequestAsync(

            promptName: "diagnostic_assistance",

            parameters: new Dictionary<string, object>

            {

                ["symptoms"] = symptoms,

                patientHistory = patientHistory,

                relevantGuidelines = _patientContext.GetRelevantGuidelines()

            });

            

        return DiagnosticSuggestion.FromMCPResponse(response);

    }

}

// 금융 위험 평가를 위한 Java MCP 서버

import org.mcp.server.*;

import org.mcp.security.*;



public class FinancialRiskMCPServer {

    public static void main(String[] args) {

        // 금융 규정 준수 기능을 갖춘 MCP 서버 생성

        MCPServer server = new MCPServerBuilder()

            .withModelProviders(

                new ModelProvider("risk-assessment-primary", new AzureOpenAIProvider()),

                new ModelProvider("risk-assessment-audit", new LocalLlamaProvider())

            )

            .withPromptTemplateDirectory("./compliance/templates")

            .withAccessControls(new SOCCompliantAccessControl())

            .withDataEncryption(EncryptionStandard.FINANCIAL_GRADE)

            .withVersionControl(true)

            .withAuditLogging(new DatabaseAuditLogger())

            .build();

            

        server.addRequestValidator(new FinancialDataValidator());

        server.addResponseFilter(new PII_RedactionFilter());

        

        server.start(9000);

        

        System.out.println("Financial Risk MCP Server running on port 9000");

    }

}

// TypeScript: MCP 서버에 Playwright 브라우저 자동화 도구 등록

import { createServer, ToolDefinition } from 'modelcontextprotocol';

import { launch } from 'playwright';



const server = createServer({

  name: 'Playwright MCP Server',

  version: '1.0.0',

  description: 'MCP server for browser automation using Playwright'

});



// URL로 이동하고 스크린샷을 캡처하는 도구 등록

server.tools.register(

  new ToolDefinition({

    name: 'navigate_and_screenshot',

    description: 'Navigate to a URL and capture a screenshot',

    parameters: {

      url: { type: 'string', description: 'The URL to visit' }

    }

  }),

  async ({ url }) => {

    const browser = await launch();

    const page = await browser.newPage();

    await page.goto(url);

    const screenshot = await page.screenshot();

    await browser.close();

    return { screenshot };

  }

);



// MCP 서버 시작

server.listen(8080);

# Example: Azure MCP server deployment configuration (YAML)

apiVersion: mcp.microsoft.com/v1

kind: McpServer

metadata:

  name: enterprise-mcp-server

spec:

  modelProviders:

    - name: azure-openai

      type: AzureOpenAI

      endpoint: https://<your-openai-resource>.openai.azure.com/

      apiKeySecret: <your-azure-keyvault-secret>

  tools:

    - name: document_search

      type: AzureAISearch

      endpoint: https://<your-search-resource>.search.windows.net/

      apiKeySecret: <your-azure-keyvault-secret>

  authentication:

    type: EntraID

    tenantId: <your-tenant-id>

  monitoring:

    enabled: true

    logAnalyticsWorkspace: <your-log-analytics-id>