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108,874 tools. Last updated 2026-04-17 00:21
  • 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.
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  • 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.
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  • 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.
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  • 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")
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  • 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.
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