Three Approaches, One Goal
To integrate AI into your development workflow, three main architectures are available: skills (instruction files), MCP servers (Model Context Protocol), and direct APIs. Each has its strengths and optimal use cases.
Skills: Behavioral Guidance
What Is a Skill?
A skill is an instruction file (Markdown) that configures AI behavior. It does not make external calls, does not execute code: it guides the AI responses.
When to Use Skills
- Code conventions: Style, naming, architecture
- Workflows: Development processes, code review
- Context: Tech stack, project constraints
- Persona: Expected role and expertise from the AI
Strengths
- Zero technical configuration
- No external dependencies
- Easy to share and version control
- Works offline
- No network security risk
Limitations
- Cannot access external data
- Cannot execute actions
- Static (no real-time data)
MCP Servers: Capability Extension
What Is MCP?
The Model Context Protocol is an open protocol that allows AI to connect to data sources and external tools. An MCP server exposes functionalities that AI can call.
When to Use MCP
- Data access: Databases, files, third-party APIs
- Actions: Ticket creation, message sending, deployments
- Dynamic context: Real-time data, system state
- Integrations: Jira, Slack, GitHub, databases
MCP Server Examples
- GitHub MCP: Issue and PR management
- Database MCP: Direct SQL queries
- Slack MCP: Send and read messages
- File System MCP: File system access
Strengths
- Access to dynamic data
- Concrete actions (not just text)
- Standardized protocol
- Growing ecosystem
Limitations
- Technical configuration required
- Network dependencies
- Larger security attack surface
- External call latency
Direct APIs: Total Control
What Is the API Approach?
Direct integration via model APIs (Claude API, OpenAI API) to build custom AI pipelines in your application.
When to Use APIs
- Products: AI features in your application
- Pipelines: Automated data processing
- Agents: Complex autonomous systems
- Customization: Total control over prompt and flow
Strengths
- Total control over behavior
- Native integration in your application
- Scalability
- Deep customization
Limitations
- Significant development effort
- API cost management
- Integration code maintenance
- Technical expertise required
Comparison Table
| Criteria | Skills | MCP | API | |---|---|---|---| | Setup complexity | Very low | Medium | High | | External data access | No | Yes | Yes | | Possible actions | No | Yes | Yes | | Cost | Free | Variable | Pay-per-use | | Security | Excellent | Good | Variable | | Maintenance | Minimal | Medium | High | | Flexibility | Medium | Good | Maximum | | Offline | Yes | No | No |
The Recommended Hybrid Architecture
In practice, the best teams combine all three approaches:
Layer 1: Skills for Guidance
# CLAUDE.md
@company-standards.md
@project-context.md
@code-conventions.md
Skills handle everything static and behavioral: conventions, style, processes.
Layer 2: MCP for Integrations
{
"mcpServers": {
"github": { "command": "mcp-github" },
"database": { "command": "mcp-postgres" },
"jira": { "command": "mcp-jira" }
}
}
MCP handles everything requiring access to external systems.
Layer 3: APIs for Products
const response = await anthropic.messages.create({
model: "claude-sonnet-4-20250514",
messages: [{ role: "user", content: prompt }],
system: systemPrompt
});
APIs handle AI features in your product.
Decision Criteria
Use skills when:
- You want to standardize AI behavior
- No need for external data
- You want something simple and fast
- The team needs to share conventions
Use MCP when:
- You need access to external systems
- The AI must perform concrete actions
- Context must be dynamic and up to date
- You use Claude Code or a compatible IDE
Use APIs when:
- You are building a product with integrated AI
- You need total control
- Request volume justifies the investment
- You have a dedicated technical team
Conclusion
There is no single solution. The right architecture depends on your specific needs. Start with skills (the simplest), add MCP when you need integrations, and move to APIs when building a product.
Explore our skills library to get started and check our technical guides to dive deeper into each approach.