MCP Chain of Draft Server

The MCP Chain of Draft Server addresses a common pain point for AI‑enabled development teams: how to transform raw model outputs into polished, reliable artifacts. Traditional single‑pass generation often yields solutions that are incomplete or riddled with subtle errors, especially in complex domains like API design, architectural planning, or code review. This server implements the Chain of Draft protocol—a structured, iterative refinement process that lets an AI assistant progressively improve its reasoning and output across multiple drafts. By exposing this workflow as an MCP server, developers can embed systematic thinking directly into their existing AI toolchains, ensuring that every iteration is tracked, critiqued, and logged.

At its core, the server offers a suite of iterative reasoning capabilities. Each draft is stored in a thought history that the assistant can reference, enabling context‑aware updates and preventing regressions. The protocol supports branching, allowing reviewers to focus on specific reasoning steps or critique dimensions—such as logical consistency, performance implications, or security concerns—without re‑generating the entire chain. This fine‑grained control is especially valuable when collaborating across teams or integrating with continuous integration pipelines.

The server is built with TypeScript and Zod validation, ensuring that all inputs and outputs conform to strict schemas. This type safety not only reduces runtime errors but also makes it easier for developers to reason about the data flow between tools. Comprehensive error handling and a built‑in real‑time logging system provide visibility into each step of the refinement process, which is critical for debugging and auditability in regulated environments.

Typical use cases include:

• API Design: Drafting and iteratively refining endpoint specifications, with each draft validated against OpenAPI schemas.
• Architecture Decision Records: Generating initial high‑level designs and then progressively tightening constraints (e.g., scalability, cost) through focused critiques.
• Code Review Automation: Producing a first pass of refactored code and then iteratively polishing it based on static analysis feedback.
• Implementation Planning: Breaking down feature requirements into actionable tasks, with each draft adding more detail and clarifying dependencies.

Integration is straightforward: any MCP‑compatible AI assistant can call the server’s tools, passing a current draft and receiving an updated version along with critique metadata. Because the protocol is language‑agnostic, it can be paired with agents written in Python, Go, or JavaScript without modification. The server’s lightweight runtime (Node.js ≥16) and modular project structure make it easy to embed in existing microservice architectures or to run locally for rapid prototyping.

What sets this MCP server apart is its combination of structured reasoning, type safety, and real‑time observability. By formalizing the iterative refinement process, it turns nebulous “thinking” into a repeatable workflow that developers can audit, test, and automate. This not only improves the quality of AI‑generated artifacts but also builds trust among stakeholders who need to see how conclusions were reached and refined over time.