Code2Paper for IBM Bob + DocSync MCP

Modern AI-assisted development is rapidly shifting toward coding agents and autonomous workflows, but current AI systems still suffer from a major structural limitation: their knowledge becomes outdated faster than the ecosystem evolves. During development, I repeatedly observed coding agents generating deprecated SDK integrations, obsolete model references, and outdated API patterns even after explicit instructions were provided. For example, when instructed to use the latest Gemini SDK patterns and models such as gemini-3.1-flash-lite, many coding assistants still reverted to older implementations like gemini-1.5 or deprecated SDK syntax. The issue was not reasoning capability — it was the static nature of LLM training data versus the rapidly evolving AI ecosystem. To solve this, I built DocSync MCP, a real-time documentation intelligence system for IBM Bob. DocSync continuously scrapes official SDK documentation, indexes it into a vector database, retrieves live implementation patterns, and exposes them through MCP tools directly inside Bob’s reasoning loop. Before generating SDK-specific code, Bob can search live docs, retrieve current APIs, and query live model catalogs from providers such as Google, OpenAI, and Anthropic. This grounds code generation on real-time ecosystem intelligence instead of outdated training memory. Alongside DocSync, I also built Code2Paper, a custom orchestration mode for IBM Bob that transforms a working research repository into a publication-ready research paper. Code2Paper analyzes repositories, identifies novelty, performs federated literature search, generates architecture diagrams, plots, and comparison tables, drafts sections using venue-specific Typst templates, and compiles complete papers for conferences such as NeurIPS, CVPR, and IEEE. Together, these systems solve two connected problems: keeping AI coding agents aligned with rapidly evolving technologies, and automating scientific communication directly from codebases.