MCP-Typescribe

MCP‑Typescribe bridges the gap between large language models (LLMs) and modern TypeScript codebases by offering a real‑time, machine‑readable view of API documentation. The server parses TypeDoc JSON files—generated from any TypeScript project—and exposes a rich set of query endpoints that conform to the Model Context Protocol. This design lets LLM‑powered assistants, such as Claude or Cursor, ask for symbol details, search by return type, discover implementations, and trace usage patterns without needing to ingest the entire documentation into their prompt. The result is a far more efficient workflow where the AI can focus on high‑level reasoning while delegating low‑level API lookup to the server.

The core problem MCP‑Typescribe addresses is the invisibility of newly released or internal SDKs to LLMs. Because these models are trained on static corpora, they lack knowledge of fresh APIs unless the entire documentation is provided as prompt context—a strategy that quickly becomes unwieldy for large libraries. By indexing the API into a searchable graph, developers can let their assistants discover and learn about unfamiliar symbols on demand. This reduces the lag between an API’s release and its adoption in automated coding, accelerating innovation across teams.

Key capabilities include:

• TypeDoc integration that ingests JSON outputs from the TypeScript compiler, preserving type information and JSDoc comments.
• Comprehensive query tools such as , , and that expose the API’s structure, relationships, and documentation to the agent.
• MCP compliance ensures seamless interoperability with any tool that speaks the protocol, from Claude’s custom prompts to open‑source agents like Windsurf.

Typical use cases span internal tooling, where a company’s proprietary SDK needs to be consumed by an AI assistant without retraining the model; onboarding new developers, who can explore unfamiliar APIs through conversational queries; and rapid prototyping, where the assistant can suggest correct method signatures by searching the live API graph. The server’s lightweight nature also makes it ideal for CI pipelines that generate fresh TypeDoc JSON after each build, keeping the assistant’s knowledge current.

In summary, MCP‑Typescribe delivers a plug‑and‑play solution that transforms static TypeScript documentation into an interactive knowledge base. By enabling LLMs to query API details in real time, it empowers developers with faster onboarding, more accurate code generation, and a step toward truly autonomous, context‑aware coding agents.