Large language models (LLMs) possess a fundamental limitation that is rarely framed as an engi-neering problem with a tractable solution: the knowledge cutoff. Frameworks, libraries tools releasedafter a model’s training horizon are effectively invisible to it, producing hallucinated APIs, brokenclass references non-compiling code. This paper argues that the Model Context Protocol (MCP) canserve not just as a documentation delivery mechanism but as a self-bootstrapping distribution chan-nel that simultaneously resolves three distinct problems: (1) the knowledge-cutoff problem for newlypublished software; (2) the forward-reference problem, enabling AI agents to generate correct codeagainst specifications that predate their implementations; and (3) the open-source distribution prob-lem, offering a structured, machine-readable release pipeline that delivers source, binaries semanticmetadata as a unified artifact. We describe the architecture of such a system, formalize its protocolcontracts evaluate its implications for the open-source community. The proposal is grounded in aworking prototype and is presented here as a general method inviting adoption, experimentationcommunity extension.