mcp-typescribe An MCP server implementation enabling LLMs to work with new APIs and frameworks Give your AI a memory Build one knowledge base all your AI tools draw from, kept current with your review. Course Get the Starter Kit README The Problem Large Language Models (LLMs) have made incredible strides in code generation and developer productivity. However, they face a key limitation: they can only reliably use APIs and libraries they’ve seen during training. This creates a bottleneck for adopting new tools, SDKs, or internal APIs — LLMs simply don’t know how to use them effectively. While tools can be given source code access (when interacting with APIs for which the sources are available) or access to documentation files (e.g. typescript type definition files), this doesn't scale well for large APIs. LLMs need a more efficient way to learn more about an API. Putting all the documentation into context for every request is inefficient, unfeasible, and leads to poor results. As a result: Larger new or internal APIs remain "invisible" to LLMs.