Understanding the Model Context Protocol (MCP): A Comprehensive Guide

Key Points:

• LLMs are essentially text generators – they predict and complete text based on the context given to them
• Modern LLMs output text in structured formats like JSON to make interaction more organized
• LLMs can’t directly interact with external systems – they can only generate text
• Function/tool calling is a way for LLMs to request actions by generating specifically formatted text
• MCP is a JSON RPC protocol that standardizes how LLMs can discover and call external functions

My Take:

The beauty of MCP is its simplicity – it’s essentially a standardized way for AI models to ask “what functions do you have available?” and then call them in a structured manner. This deceptively simple concept opens up enormous possibilities for extending AI capabilities while maintaining a clean separation of concerns.

Key Points:

• With a fetch MCP server, Claude can access and read content from websites
• Adding a Brave search server allows Claude to search the web for current information
• Google Maps server enables Claude to find locations, get directions, and find nearby places
• Without these tools, Claude (like other LLMs) is limited to the information it was trained on
• Each tool must ask for permission before executing, providing a security layer

My Take:

These examples clearly illustrate the transformative power of MCP. What’s particularly impressive is how seamlessly the AI assistant integrates these external capabilities into its conversational flow. The user doesn’t need to know the underlying mechanisms – they simply ask questions naturally, and the AI figures out which tools to use.

Key Points:

• Traditional MCP server implementations have several drawbacks:
• API keys stored in plaintext configuration files
• Tools have unrestricted access to your machine
• Docker solutions are resource-heavy
• MCP.Run addresses these issues by:
• Using WebAssembly for sandboxing serlets
• Securely encrypting and storing API keys
• Allowing fine-grained permissions for network and file system access
• Supporting profiles to manage different sets of tools

# Example of configuring MCP.Run in Claude Desktop
mcpx@latest
    

My Take:

The security concerns Eduardo addresses are critical for widespread MCP adoption. By leveraging WebAssembly, MCP.Run provides a clever solution that balances functionality, security, and performance. The ability to have fine-grained control over what each serlet can access is particularly valuable in production environments.

Key Points:

• Creating a new serlet involves registering it on the MCP.Run platform
• The XTP command-line tool handles building and publishing serlets
• Serlets must implement two key functions:
• describe() – returns schemas for all available functions
• call() – implements the actual functionality for each tool
• Once published, serlets can be immediately installed in any profile

# Creating a new serlet with XTP
xtp plugin create hello-world

# Building and publishing the serlet
xtp plugin build
xtp plugin push
    

My Take:

The serlet creation process is surprisingly straightforward, especially considering the power it unlocks. While the demo shows a simple example, the same pattern could be used to create serlets that interact with databases, APIs, or complex business systems. This extensibility is what makes MCP truly powerful.

Key Points:

• Special Kubernetes serlets allow querying deployments, pods, and services
• Custom application serlets can interact with APIs running in the cluster
• The demo shows Claude diagnosing a failing deployment and suggesting fixes
• Currently uses kubectl proxy for cluster access, with certificate-based auth coming soon
• Serlet permissions can be limited to specific base URLs for security

My Take:

The Kubernetes integration showcases the potential for AI-powered DevOps assistance. Imagine being able to ask in plain English about the status of your deployments, diagnose issues, or even automate remediation – without needing to remember complex kubectl syntax. This could dramatically change how we manage cloud infrastructure.

Key Points:

• Tasks run serlets and LLMs with defined prompts in the background
• They can be scheduled or triggered via webhook URLs
• Demo shows creating a news summarization task
• Webhook integration with GitHub automatically summarizes pull requests
• Telegram bot integration demonstrates conversational interface with MCP tools

My Take:

The automated tasks feature takes MCP beyond just interactive chat interfaces and into the realm of AI agents that can work autonomously. The GitHub PR summary example is brilliant – it solves a real pain point (PRs with insufficient descriptions) while demonstrating how AI can be embedded into existing workflows.

Key Points:

• MCPX libraries are available for different languages (Java, TypeScript, Python)
• Spring AI demo shows integrating MCP with Java applications
• Android demo with Gemini demonstrates mobile integration
• Multi-modal capabilities allow combining text and image inputs
• Pure Java implementation (Chikory) enables running on any Java platform

// Example of using MCPX4J with Spring AI
@Configuration
public class McpxConfig {
    @Bean
    public McpxForJ mcpxForJ() {
        return new McpxForJ(
            McpxConfig.builder()
                .apiKey("your-mcp-run-api-key")
                .profileId("your-profile-id")
                .build()
        );
    }
}
    

My Take:

The client libraries make MCP accessible to developers beyond just chat interfaces. This opens the door to embedding AI capabilities with tool access into practically any application – from mobile apps to enterprise systems. The multi-modal capabilities are particularly exciting, as they allow combining visual understanding with tool execution.