Scaffold MCP Server

Scaffold is a purpose‑built Retrieval‑Augmented Generation (RAG) server that turns a monolithic codebase into an intelligent, queryable knowledge graph. By parsing source files and extracting entities such as classes, functions, modules, and dependencies, Scaffold stores both vector embeddings and graph relationships in a Neo4j database. This dual representation gives AI assistants the ability to understand not only what code exists, but how it is connected—enabling precise context injection for large language models (LLMs) and other AI agents.

The server solves three common pain points in modern software teams. First, it eliminates the need for stale or manually curated documentation by automatically generating up‑to‑date structural insights. Second, it removes the “context blindness” of LLMs: instead of feeding large blocks of raw code, an assistant can query Scaffold for the exact sub‑graph relevant to a task, ensuring that generated suggestions are grounded in the real architecture. Third, it centralizes knowledge that would otherwise be lost when developers leave; the graph remains as a persistent artifact that new team members can query immediately.

Key capabilities include: graph construction from arbitrary languages, vector‑based semantic search, context‑aware prompt generation, and refactoring assistance that can propose changes across multiple files while preserving dependency integrity. Scaffold exposes these functions through the Model Context Protocol, allowing AI clients to request tailored snippets or entire modules with a single API call. The integration is seamless: an assistant can fetch the nearest code snippets for a given feature, run them through an LLM for explanation or modification, and then push updated code back to the repository—all orchestrated by Scaffold’s context layer.

Real‑world use cases span automated code reviews, on‑boarding new developers, and continuous integration pipelines that enforce architectural consistency. In a CI/CD workflow, for example, Scaffold can surface all files affected by a change and provide context‑rich explanations to the LLM, which then generates unit tests or refactoring suggestions. For documentation generation, Scaffold can supply the exact code snippets that illustrate a concept, ensuring that docs stay synchronized with implementation.

What sets Scaffold apart is its lightweight, container‑friendly deployment model. A single Docker image, coupled with a pre‑configured Neo4j and Chroma instance, lets teams spin up the entire stack in minutes. The server’s MCP interface abstracts away database details, presenting developers with a straightforward, language‑agnostic API that can be integrated into existing toolchains or custom AI agents. This combination of automated graph generation, context‑aware querying, and effortless deployment makes Scaffold a powerful ally for any team looking to harness AI while keeping their codebase intelligible and maintainable.