AI Distiller MCP Server

AI Distiller () is an MCP‑enabled server that transforms a sprawling codebase into a lean, AI‑friendly knowledge base.
When developers ask Claude or other LLMs to write, refactor, or debug code, the models are constrained by a limited context window. In practice this means they can only “see” a few hundred lines of source at a time, leading to hallucinated APIs, incorrect type signatures, and brittle implementations. tackles this bottleneck by performing code distillation: it scans the entire project, extracts only the public interface—class names, method signatures, type annotations, and module dependencies—and discards implementation details that are irrelevant for the AI’s reasoning. The resulting distilled snapshot is typically 5–20 % of the original size, yet contains all the structural information an assistant needs to generate correct code on the first attempt.

The server exposes a rich set of MCP capabilities that make it easy to plug into existing AI workflows. Developers can invoke the endpoint from Claude Desktop, Cursor, or any MCP‑compatible client and receive a JSON payload that includes:

• Resources – the distilled code structure, ready for inclusion in a prompt.
• Tools – helper commands such as “list public functions” or “show type hints” that can be called on demand.
• Prompts – pre‑built prompt templates that guide the model to use the distilled context effectively.
• Sampling – fine‑grained control over token limits and temperature settings to match the target LLM’s capabilities.

Real‑world scenarios where shines include large monorepos, polyglot projects with 12+ languages, and continuous‑integration pipelines that require on‑the‑fly code generation. By integrating into a CI job, a team can automatically generate unit tests or documentation that respect the exact public API of their codebase, eliminating the guesswork that often leads to merge conflicts or runtime failures. In a research setting, the distilled context can be fed into an LLM to perform static analysis or generate type‑annotated wrappers for legacy code.

What sets apart is its dependency‑aware distillation. It understands module imports and cross‑file references, ensuring that the distilled snapshot preserves the true public contract of each component. Additionally, the tool offers a Git‑history analysis mode that can surface changes over time, allowing developers to feed the AI only the most recent API modifications. The result is a highly efficient, repeatable workflow where AI assistants can operate with full awareness of the project’s architecture without being overwhelmed by its size.