Chapter 4

In-depth analysis of core technologies

Gain a deep understanding of the core technologies behind AI programming tools, including MCP, Skill systems, Agent systems, and LSP, and master the architectural principles and configuration methods of these technologies.

MCP（Model Context Protocol）

MCP is a standard protocol that connects AI models and external services, enabling AI to access resources such as file systems, databases, and APIs.

Architectural principles

MCP adopts a client-server architecture:

  • • MCP Client: AI tools (such as Cursor, Claude Code)
  • • MCP Server: The backend that provides services (file system, GitHub, database)
  • • Protocol: standardized communication protocol (JSON-RPC)
  • • Tools & Resources: Tools and resources provided by Server

Common MCP Servers

File system

Read and write local files

GitHub

GitHub API integration

Database

SQL query execution

Configuration and Security

  • • Configuration file: Configure MCP Servers in JSON format
  • • Authentication mechanism: authentication methods such as API Key, OAuth, etc.
  • • Access control: Limit the access scope of the Server
  • • Security best practices: Key management, principle of least privilege

Custom MCP Server

Create your own MCP Server to extend AI capabilities:

  • • Define Tools: Implement tool functions callable by AI
  • • Provide Resources: Expose data resources to AI
  • • Implement the protocol: Comply with the MCP protocol specification
  • • Testing and deployment: Deploy to production after local testing

Skill system

The Skill system gives AI assistants reusable specialized capabilities and is the key to improving the efficiency of AI tools.

Cursor Skill

  • • Markdown format definition
  • • Context and instructions
  • • Reusable capabilities
  • • Version management

Kiro Steering Files

  • • Configuration file-driven
  • • AI behavior customization
  • • Project-specific configuration
  • • Team sharing

Claude Code Skills

  • • Command-line only
  • • Terminal integration
  • • Workflow automation
  • • MCP integration

Meta-skills

  • • Generate other skills
  • • Skill Template
  • • Automatic creation
  • • Best practices

Team Skill Management

  • • Skill library：Share the Skill library across the team and standardize the rules
  • • Version control: version-manage Skill like code
  • • Documentation: Each Skill has clear documentation
  • • Review mechanism: Skill submission requires team review

Agent system

The Agent system enables AI to autonomously execute complex tasks, rather than just respond interactively.

Agent Types

Reactive Agent

Responsive, based on current state

Planning Agent

Planning-oriented, formulate an execution plan

Learning Agent

Learning-oriented, improving from experience

Workflow

1
Understand the task: Analyze user needs and understand the task objectives
2
Make a plan: break down the task and define execution steps
3
Execute action: Call tools, modify files, execute commands
4
Evaluation results: Check the execution results and decide the next step

Multi-agent orchestration

  • • Architect Agent: responsible for system design and architecture decisions
  • • Coding Agent: responsible for code implementation and writing
  • • Test Agent: Responsible for writing and executing test cases
  • • Review Agent: responsible for code reviews and quality checks

Plan System

The Plan system enables Agents to autonomously formulate and execute plans:

  • • Task decomposition：Break complex tasks into executable steps
  • • Dependency management: Understand the dependencies between steps
  • • Dynamic adjustment: adjust the plan based on execution results
  • • Progress tracking: Track task execution progress in real time

LSP（Language Server Protocol）

LSP is the standard protocol between IDEs and language services, and AI IDEs enhance intelligent capabilities on top of it.

Architectural principles

1
Language Server: Backend providing language services (completion, jump, diagnostics)
2
Language Client：IDE client, sends requests and displays results
3
Protocol: JSON-RPC protocol, standardized communication

Relationship with AI IDE

  • • LSP provides basic language services
  • • AI-enhanced completion and diagnostics
  • • Understand in context
  • • Intelligent code generation

Language service capabilities

  • • Code completion: Smart autocomplete, context-aware
  • • Go to definition: Quick jump to the definition location
  • • Diagnosis：Real-time error checking and warnings
  • • Formatting: Automatic code formatting

Learning outcomes

After completing this chapter, you will:

  • 1Deeply understand the core mechanisms and architectural principles of MCP, Skill, Agent, and LSP
  • 2Able to configure and customize these systems, and create your own MCP Server and Skill
  • 3Understand the underlying architecture of AI IDEs and master multi-Agent orchestration and the Plan system
  • 4Understand team skill management and how LSP and AI work together
Previous chapter: Detailed explanation of command-line toolsNext chapter: Fabric AI enhancement framework