Search through Apple's complete developer documentation, including API references, code examples, WWDC session transcripts, and Human Interface Guidelines across all Apple platforms.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@apple-docs-mcp-server Show me SwiftUI animation code examples"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Search through Apple's complete developer documentation using your AI coding assistant.
This tool connects your AI assistant (like Cursor) to Apple's entire developer documentation. Instead of manually browsing developer.apple.com, your AI can search through Apple's docs and provide precise answers with source links.
Complete documentation coverage:
All Apple platforms: iOS, macOS, watchOS, tvOS, and visionOS - from early versions to current iOS 26, macOS 26, watchOS 26, tvOS 26, visionOS 26
WWDC session transcripts from 2019-2025 (including latest WWDC 2025 with Liquid Glass API, Foundation Models Framework)
Complete API references for all frameworks - legacy APIs, current APIs, and newest additions
Code examples with context and explanations across all versions
Human Interface Guidelines (HIG) - Apple's complete design guidelines for all platforms
How it works: Natural language search - ask questions in plain English. Type "how to animate a button press" or "best practices for iOS navigation" and get relevant answers. The search understands concepts and context, not just exact keyword matches.
Database: 16,253+ documents with complete content coverage.
Related MCP server: Apple Dev MCP Server
npm install apple-docs-mcp-serverThe documentation database (260MB) downloads automatically during installation.
1. Create .cursor/mcp.json in your project root:
{
"schemaVersion": 1,
"mcpServers": {
"apple_docs": {
"command": "/absolute/path/to/node_modules/apple-docs-mcp-server/run-mcp-safe.sh",
"env": {
"OPENAI_API_KEY": "your_openai_api_key_here"
},
"autoStart": true,
"alwaysAllow": ["search_docs", "get_doc", "get_code_examples", "get_stats"],
"timeout": 30000
}
}
}2. Find your correct path:
# In your project directory:
echo "$(pwd)/node_modules/apple-docs-mcp-server/run-mcp-safe.sh"3. Restart Cursor completely
Ask your AI assistant questions about Apple development:
API & Code examples (any version):
"Show me SwiftUI animation code examples"
"How do I use Core Data with CloudKit synchronization?"
"Foundation Models Framework setup and usage examples"
"What's new in UIKit for iOS 26?"
Design & Interface Guidelines:
"Apple's guidelines for button design in iOS apps"
"Best practices for navigation patterns on iPad"
"Accessibility requirements for app icons"
Natural language queries:
"How to handle memory management in SwiftUI?"
"How to implement Liquid Glass effects in my app?"
"Best way to implement dark mode support"
Your AI searches through Apple's complete documentation and provides answers with source links. When documents contain code examples, your AI can extract those examples with full context and explanations.
Complete historical and current coverage: This is the most comprehensive Apple developer documentation collection available. Everything Apple has ever published and continues to publish - from legacy APIs to cutting-edge frameworks, all WWDC sessions from 2019-2025, complete Human Interface Guidelines - all in one searchable database.
Natural language search: Ask questions in plain English. No need to know exact API names or keywords. The search understands what you're trying to accomplish.
Working code examples: Not just documentation text, but actual Swift code with explanations and context across all platform versions.
Fast and local: All documents are processed on your machine. Search results appear instantly without API calls.
From legacy to latest: Whether you need legacy iOS 13 APIs or the newest iOS 26 features like Liquid Glass API - it's all here with complete WWDC 2025 content.
Cursor shows "No tools":
Use absolute paths in .cursor/mcp.json
Make sure run-mcp-safe.sh is executable: chmod +x run-mcp-safe.sh
Restart Cursor completely
OpenAI API key errors:
Put your API key in the env section of mcp.json (recommended)
Check API key format: should start with sk-proj- or sk-
Database errors:
Reinstall if database is missing: npm uninstall apple-docs-mcp-server && npm install apple-docs-mcp-server
Check database file exists: ls -lh node_modules/apple-docs-mcp-server/embeddings.db
search_docs: Search through all Apple documentation and design guidelines
get_doc: Get complete document content by ID
get_code_examples: Extract working Swift code examples from documentation with explanations
get_stats: Database statistics and information
Node.js 18 or later
OpenAI API key
Cursor or compatible MCP client
MIT License - see LICENSE file for details.
This is an unofficial tool for accessing Apple developer documentation. No affiliation with Apple Inc.
đź”— 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:
search_docs("SwiftUI navigation") → find relevant documents
See result shows "codeBlocks: 7"
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.
Inputs: docId (required).
đź“– 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.
Inputs: id (required).
đź“Š 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.
Inputs: no named parameters.
🔍 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:
âś… GOOD: "SwiftUI custom transition animation", "Core Data CloudKit conflict resolution"
âś… 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.
Inputs: query (required), limit, minSimilarity, includeContent, +3 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 4 tools
Each tool has a clearly distinct purpose: stats overview, semantic search, full document retrieval, and code extraction. There is no overlap between search_docs, get_doc, and get_code_examples, as each serves a specific step in the documentation workflow.
All tool names follow a consistent verb_noun pattern with snake_case: get_stats, search_docs, get_doc, get_code_examples. The verbs and nouns are predictable and accurately reflect the tool's function.
With 4 tools, the server is well-scoped for its purpose of searching and retrieving Apple documentation. Each tool is essential and the small count keeps the surface manageable without feeling sparse.
The server covers the full lifecycle of accessing documentation: understanding scope (get_stats), searching (search_docs), reading full content (get_doc), and extracting code examples (get_code_examples). For a read-only documentation server, this is a complete and practical set with no obvious dead ends.
Apple developer docs and WWDC transcripts with RAG retrieval and Jev relevance ranking.
Search and read Apple developer documentation, HIG, and WWDC transcripts as Markdown.
Search and read the Droppy for Mac handbook, droplets, DroppyKit SDK and release notes.
Search and read tested Swift, JavaScript, CSS and HTML docs from DevDocs10.
