From Intelligent to Agentic Applications

From Intelligent to Agentic Applications: Using Model Context Protocol to Support Agentic Behaviors in Your Application

• Kyle Stratis
  • Homepage Very intersting!!
  • LinkedIn
  • Book
    • AI Agents with MCP
• slides

NoteNotes

• Why the book was written
  • Kyle began writing the book after struggling to build his first MCP client in early 2025.
  • At the time, MCP documentation was heavily focused on servers, while guidance for building clients was sparse.
  • His goal is to provide a practical and architectural reference for MCP, especially in Python.
  • The book covers MCP servers, clients, security, transports, implementation details, and deployment patterns.
• What MCP does
  • MCP decouples tools, prompts, and data from the agent application itself.
  • This prevents developers from having to rebuild the same tools separately for each agent or framework.
  • MCP allows service providers to expose agent-ready interfaces in a standardized way.
  • In Kyle’s framing, MCP is especially useful for distributing tools and capabilities across teams, products, and enterprise users.
• Agent-building advice
  • Standard software engineering principles still matter: code quality, maintainability, and avoiding unnecessary duplication.
  • The newer challenge with agents is their broader attack surface.
  • Because agents operate over natural language and may access many systems, they are more vulnerable to misuse or prompt-driven failures.
  • Kyle highlights Simon Willison’s “lethal trifecta”: combining access to private data, external communication, and code execution can create serious data-exfiltration risks.
  • A safer architecture separates these capabilities, for example through separate agents, coordinators, or sub-agents.
• Evaluation and observability
  • Agent evaluation should happen both during development and in production.
  • Kyle recommends development-time evaluation tools such as Promptfoo for testing tool choice, expected behavior, and likely user scenarios.
  • He also stresses the need for tracing and observability to understand what users actually ask and how the agent responds in real conditions.
• Python command-line integration
  • The workshop’s Python code interacts with the command line using standard Python patterns.
  • The if __name__ == "__main__" construct allows the script to be run directly from the command line.
  • Basic functions such as input() and print() are sufficient for simple command-line interaction.
  • For richer command-line interfaces, Kyle mentions tools such as Click, Typer, and Argparse, with a preference for purpose-built packages over raw Argparse for more polished user interfaces.
• Skills versus MCP
  • Kyle sees skills as useful, especially for personal or small-team workflows.
  • Skills are essentially selectively invoked prompts, and they work well for tasks such as code review, editing, drafting, note expansion, and flashcard generation.
  • Their limitations include weaker dependency management, lack of straightforward version pinning, and model-specific behavior.
  • MCP is broader: it can expose prompts, tools, resources, data, and more advanced interaction patterns.
• When to use a skill
  • Use a skill when the task is primarily prompt-based.
  • Skills are appropriate for lightweight personal automation or small-team workflows.
  • They are a good first step because many useful agent behaviors can be achieved with a well-written prompt.
  • Example: using Claude Code and personal notes to generate language-learning flashcards.
• When to use an MCP server
  • Use an MCP server when the agent needs stronger distribution, authentication, versioning, or integration with external systems.
  • MCP is better when exposing tools, data, or API-like capabilities to many users.
  • It is also preferable for advanced interaction patterns, such as elicitation, where the server needs to request user input to complete its work.
  • Kyle’s rule of thumb: if the agent interaction resembles calling or exposing an external API, an MCP server is usually the better abstraction.
• Main takeaway
  • MCP is presented as an infrastructure layer for scalable, reusable, and secure agent capabilities.
  • Skills are lightweight prompt-level extensions; MCP servers are more formal, distributable, and suitable for tool- and data-rich agent ecosystems.
  • Building effective agents requires not only tool access, but also careful security architecture, evaluation, and runtime observability.