Alias for `get_nav_series` with explicit pension NAV semantics for tool-RAG routing.

Search Apple's official developer documentation and video content using advanced RAG technology. Returns relevant content from Apple's technical documentation, frameworks, APIs, design guidelines, and educational resources.

Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

Find AI/ML tools and libraries by describing what you need in plain English. Searches 220K+ indexed AI repos via semantic + keyword search. Optional domain filter: mcp, agents, ai-coding, rag, llm-tools, generative-ai, diffusion, voice-ai, nlp, computer-vision, embeddings, vector-db, prompt-engineering, transformers, mlops, data-engineering, ml-frameworks Examples: find_ai_tool("database query tool for postgres", domain="mcp") find_ai_tool("autonomous coding agent") find_ai_tool("PDF document chunking for RAG pipeline")

Extract metadata (Open Graph, Twitter Cards, favicon, JSON-LD, body text, redirect chain, HTTP status, content type, etc.) from any URL. Use fields to select specific data — include "body" for clean page text (ideal for RAG/embeddings), "statusCode" and "contentType" for HTTP response metadata. Response includes redirectChain showing every HTTP redirect hop.