# Phase 1: Discovery & Language Selection

**Purpose**: Understand what the user wants to build and choose the right SDK

## Questions to Ask

### 1. Server Purpose
- "What data source, tools, or workflows do you want to expose to AI?"
- Examples: "Access PostgreSQL database", "Search Jira tickets", "Format code"

### 2. Target AI Application
- Claude Desktop (most common)
- Custom AI application
- Multiple clients

### 3. Programming Language Preference
- **TypeScript/Node.js** (recommended for web APIs, JavaScript ecosystem)
- **Python** (recommended for data processing, ML workflows)
- **Java/Spring AI** (enterprise Java applications)
- **Kotlin** (Android/JVM applications)
- **C#/.NET** (Windows/Azure applications)

### 4. Capability Types Needed
- **Tools**: Functions AI can call (e.g., "get_weather", "search_database")
- **Resources**: Data AI can read (e.g., file contents, API responses)
- **Prompts**: Specialized templates for common tasks

## Output

Clear understanding of:
- Server purpose
- Language choice
- Capabilities needed (tools/resources/prompts)

## Transition

Proceed to Phase 2 (Project Structure Generation)