🔍 SEMANTIC SEARCH through 16,253 Apple documentation pages with intelligent relevance scoring.

SEARCH STRATEGY: • BROAD→NARROW: Start with general concepts ("SwiftUI animation") then narrow down ("SwiftUI keyframe animator") • ITERATE: If results aren't specific enough, reformulate with more precise terms • EXPLORE: Use limit 8-10 for initial exploration, limit 3-5 for focused searches • 🔗 RELATED MODE: Set includeRelated=true to auto-discover connected topics

QUERY EXAMPLES: ✅ GOOD: "SwiftUI custom transition animation", "Core Data CloudKit conflict resolution"
✅ GOOD: "NSFetchRequest predicate performance", "UIKit navigation controller delegate" ✅ WITH RELATED: "SwiftUI Button" + includeRelated=true → 3 main + 5 related (ButtonStyle, UIButton, design guidelines) = comprehensive topic coverage ❌ AVOID: Single words like "Button" or "Core Data" - too broad ❌ AVOID: Non-Apple terms like "React" or "Android"

RELEVANCE SCORES: • 60%+: Highly relevant, likely contains what you need • 50-59%: Good match, worth investigating
• 40-49%: Partial match, may have related information • <40%: Weak match, consider refining search

🔗 ENHANCED BY DEFAULT: Every search includes 3-6 intelligently related documents • Semantic similarity using existing embeddings (NO extra OpenAI API calls!) • Intelligent relationship classification: 🔄 Migration, 🆕 Alternatives, ⚡ Performance, 📋 Examples • Smart threshold adaptation based on main result quality (75-85% similarity scores) • Framework-aware connections: SceneKit→RealityKit, UIKit→SwiftUI, Core Image→Metal • Quality-first approach: vector-based semantic matching replaces keyword search • Use includeRelated=false to disable and get only main search results

WORKFLOW: Use search_docs for exploration → get_doc for detailed analysis → repeat with refined queries.

📖 DEEP DIVE: Get complete document content with unlimited size - perfect for thorough analysis.

CONTENT RICHNESS: • FULL TEXT: Complete Apple documentation (up to 18K+ characters) • CODE BLOCKS: Multiple Swift examples with syntax highlighting • METADATA: Content length, code block count, document type • STRUCTURE: Sections, discussions, usage examples

USAGE PATTERNS: • SINGLE DOC: Pass string ID for one document • BATCH ANALYSIS: Pass array of up to 10 IDs for comparison • FOLLOW-UP: Use after search_docs to get complete details of promising results

WHAT YOU GET: • title: Document title • url: Direct link to Apple Developer docs • content: Complete text with markdown formatting • contentLength: Size in characters for analysis • codeBlocks: Number of code examples

STRATEGY: Get full documents when search_docs snippets look promising but lack detail. No size limits - get everything you need for implementation.

📊 DATABASE OVERVIEW: Quick health check and scope understanding of the Apple documentation database.

DATABASE SCALE: • 16,253 total documents from Apple Developer documentation • text-embedding-3-large model (3072 dimensions) for semantic search • Covers iOS, macOS, watchOS, tvOS, visionOS platforms

WHAT YOU GET: • totalDocuments: Exact count of available docs • model: AI model used for semantic search quality • dimensions: Vector dimensions for search precision • sampleTitles: Random titles to understand content types

WHEN TO USE: • First interaction: Understand database scope • Debugging: Verify system is working correctly
• Context setting: Know what's available before searching • Health check: Confirm database connection

Quick reference: This database contains comprehensive Apple platform documentation with semantic search capabilities.

🔗 CONTEXTUAL CODE EXTRACTION: Extract code examples from a specific document you already found.

WORKFLOW INTEGRATION: • Use after search_docs: See codeBlocks: 5 → get_code_examples(doc_id) → get those 5 examples • Perfect for AI agents: Found interesting document → extract its code without re-searching • Contextual approach: Work with specific documents rather than broad searches

WHAT YOU GET: • All code examples from the specified document • Full context and explanations around each code block • Categorization and complexity analysis • Same rich metadata as extract_code_examples

USAGE PATTERN:

1. search_docs("SwiftUI navigation") → find relevant documents

2. See result shows "codeBlocks: 7"

3. get_code_examples(document_id) → get all 7 examples with context

PERFECT FOR: AI agents who want to drill down into specific documents after initial search.