AI Context Evolution Workshop: The Journey to MCP

This workshop demonstrates the evolution of providing context to AI models, from basic chat to function calling, and how we arrived at the need for the Model Context Protocol (MCP). This is a foundational learning experience that prepares you for the next step: diving deep into MCP itself.

🎯 Workshop Objectives

Understand the evolution of AI context provision and why MCP emerged:

1. Traditional AI: Limited to training data
2. Function Calling: Manual, predefined functions
3. Modern Tools: Better orchestration, multiple calls
4. The MCP Solution: Dynamic, discoverable, standardized context

🚀 What's Next? Dive Deep into MCP!

After completing this workshop, you'll be ready to explore the Model Context Protocol in depth. Continue your journey with:

👉 Microsoft MCP for Beginners

This comprehensive guide will teach you how to:

• Build your own MCP servers
• Integrate MCP with various applications
• Implement real-world MCP solutions
• Understand MCP's architecture and protocols

📁 Project Structure

ai-models-and-context-evolution/
├── http-examples/                  # REST Client examples showing progression
│   ├── 01_static_context_via_system_prompts.http
│   ├── 02_dynamic_context_multi_turn_conversation.http
│   ├── 03_legacy_function_calling_deprecated.http
│   ├── 04_modern_tools_current_best_practice.http
│   ├── 05_parallel_multiple_tool_execution.http
│   ├── 06_complete_tool_calling_workflow.http
│   ├── 07_assistants_api_with_document_search.http
│   └── 08_complete_rag_workflow_with_embeddings.http
├── notebooks/                      # Jupyter notebooks for deep exploration
│   ├── 01_context_methods_and_prompting.ipynb
│   ├── 02_function_calling_legacy_format.ipynb
│   ├── 03_function_calling_modern_tools.ipynb
│   ├── 04_parallel_multiple_tool_execution.ipynb
│   ├── 05_assistants_api_with_vector_stores.ipynb
│   ├── 06_complete_rag_pipeline_deep_dive.ipynb
│   └── sample_knowledge_base_document.txt
├── assets/
│   └── conference_session_data_sample.json
├── .env                            # Environment variables (create this!)
├── requirements.txt
└── README.md

🚀 Getting Started

1. VS Code Configuration

Create the .vscode/settings.json file for REST Client extension:

{
  "rest-client.environmentVariables": {
    "$shared": {
      "endpoint": "{{$dotenv AZURE_OPENAI_ENDPOINT}}",
      "apiKey": "{{$dotenv AZURE_OPENAI_API_KEY}}",
      "gpt4oDeployment": "{{$dotenv AZURE_OPENAI_GPT4O_DEPLOYMENT_NAME}}",
      "apiVersion": "{{$dotenv AZURE_OPENAI_GPT4O_API_VERSION}}",
      "embeddingsDeployment": "{{$dotenv AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME}}",
      "embeddingsApiVersion": "{{$dotenv AZURE_OPENAI_EMBEDDINGS_API_VERSION}}"
    }
  }
}

2. Environment Variables Setup

Create a .env file with your Azure OpenAI credentials:

# Azure OpenAI Configuration
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_API_KEY=your-api-key-here

# GPT-4o Deployment
AZURE_OPENAI_GPT4O_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_GPT4O_API_VERSION=2025-01-01-preview

# Embeddings Deployment
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME=text-embedding-3-large
AZURE_OPENAI_EMBEDDINGS_API_VERSION=2023-05-15

3. Install Dependencies

pip install -r requirements.txt

4. Try the HTTP Examples

• Open any .http file in http-examples
• Click Send Request in VS Code
• REST Client will auto-use .env variables

5. Explore the Notebooks

• Start with notebooks/01_context_methods_and_prompting.ipynb
• Follow the sequence to build understanding step-by-step

📚 Learning Path

Phase 1: Understanding Context Evolution

• 01_static_context_via_system_prompts.http
• 02_dynamic_context_multi_turn_conversation.http
• notebooks/01_context_methods_and_prompting.ipynb

Phase 2: Function Calling Evolution

• 03_legacy_function_calling_deprecated.http
• 04_modern_tools_current_best_practice.http
• 05_parallel_multiple_tool_execution.http
• 06_complete_tool_calling_workflow.http

Phase 3: Deep Dive Notebooks

• notebooks/02_function_calling_legacy_format.ipynb
• notebooks/03_function_calling_modern_tools.ipynb
• notebooks/04_parallel_multiple_tool_execution.ipynb

Phase 4: Document Intelligence & RAG

• 07_assistants_api_with_document_search.http
• notebooks/05_assistants_api_with_vector_stores.ipynb
• 08_complete_rag_workflow_with_embeddings.http
• notebooks/06_complete_rag_pipeline_deep_dive.ipynb

Phase 5: Ready for MCP!

Once you've completed this workshop, you'll understand the challenges and limitations of traditional approaches, making you ready to appreciate the revolutionary changes that MCP brings.

🎯 Next Step: Head to Microsoft MCP for Beginners to learn how MCP solves these challenges with:

• Dynamic tool discovery
• Standardized protocols
• Cross-application context sharing
• Seamless integration across tools and platforms

🎓 Key Learning Outcomes

By the end of this workshop, you'll understand:

• ✅ The Context Problem and its evolution
• ✅ Static & Dynamic Context techniques
• ✅ Function Calling Evolution and limitations
• ✅ Performance Implications of different approaches
• ✅ Best Practices for Tools and RAG
• ✅ Document Intelligence & Vector Search
• ✅ Why MCP emerged as the solution

🚀 Ready for MCP? After completing this workshop, you'll have the foundation to understand why MCP is revolutionary and how it solves the challenges you've experienced here.

🛠️ Technical Requirements

• Azure OpenAI with GPT-4o + text-embedding-3-large
• VS Code with REST Client extension
• Python 3.8+
• .env file with required variables
• requirements.txt installed

📞 Quick Test Checklist

• 01_static_context_via_system_prompts.http: Static prompt limitations
• 02_dynamic_context_multi_turn_conversation.http: Dynamic progression
• 04_modern_tools_current_best_practice.http: Function calling capabilities
• 07_assistants_api_with_document_search.http: Assistants + Vector Store
• 08_complete_rag_workflow_with_embeddings.http: Full RAG workflow

🎯 What Makes This Workshop Special

It's not just about calling tools—it's about understanding why context matters, how approaches evolved, and what challenges led to the creation of MCP.

❓ Frequently Asked Questions (FAQ)

🔍 Embeddings & Vector Search

Q: What are embeddings?
A: Vector representations of text capturing semantic meaning.

Q: What model should I use?
A: text-embedding-3-large for best results; use -small or ada for cost savings.

🏗️ RAG (Retrieval-Augmented Generation)

Q: When to use RAG vs Assistants?
A: RAG = control; Assistants = simplicity + memory.

Q: How to handle large docs?
A: Chunking, metadata, hierarchical summaries.

📊 Vector Stores

Q: Azure AI Search vs custom DB?
A: Azure for production; custom if specialized needs.

💰 Cost Optimization

Q: Reduce embedding cost?
A: Cache, batch, chunk smartly, use smaller models.

🔧 Technical Implementation

Q: Debug poor RAG?
A: Check embeddings, chunking, context window, prompts.

🛡️ Security

Q: How to secure vector data?
A: RBAC, encryption, managed identities.

🚀 Scaling

Q: How to scale RAG?
A: Caching, horizontal scaling, async updates.

🎯 Choosing the Right Tool

Use Case	Best Approach	Why
Real-time data	Function Calling	Live API access
Document Q&A	RAG or Assistants	Semantic search
Conversational	Assistants API	Built-in memory
Custom systems	Function Calling	Full control
Prototypes	Assistants API	Quick setup

🔗 Continue Your Journey

Ready to see how MCP revolutionizes everything you've learned here?

👉 Microsoft MCP for Beginners

This next step will show you how MCP addresses every limitation and challenge you've experienced in this workshop, providing a unified, standardized approach to AI context and tool integration.

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Ready to understand how we went from limited AI to unlimited possibilities? Let's dive in! 🚀