Augments MCP Server

The Augments MCP Server is a specialized documentation retrieval engine built for Claude Code and other Model Context Protocol (MCP) clients. It solves the common pain point of scattered, outdated framework references by providing a single, real‑time source that aggregates documentation from GitHub, official websites, and curated examples. Developers can ask an AI assistant for up‑to‑date usage patterns or troubleshooting tips, and the server returns structured, validated JSON that the assistant can embed directly into code suggestions or explanations.

At its core, the server maintains a dynamic framework registry that categorizes tools across web, backend, mobile, AI/ML, design, and other tooling domains. The registry is hot‑reloadable, so new frameworks or version updates appear instantly without downtime. When a request arrives, the server consults its multi‑source providers—GitHub API clients with rate‑limit handling and web scrapers—to fetch the latest documentation. Intelligent prioritization ranks sources by stability, ensuring that stable releases are favored over beta or development branches unless explicitly requested.

Performance is addressed through a two‑tier caching system. Time‑to‑live (TTL) values differ for stable, beta, and dev releases, while memory and disk layers keep frequently accessed data in fast‑accessible form. Smart invalidation watches for changes on source repositories, automatically refreshing stale entries and feeding analytics that help operators tune cache behaviour. This reduces latency for developers while keeping the information fresh.

The server also enhances context by combining documentation across multiple frameworks. For example, a request that involves both React and TailwindCSS will return a unified view of how the two interact, highlighting compatibility issues or best‑practice patterns. The context enrichment tool can even analyze code snippets to detect mismatches between framework versions, providing actionable guidance.

Integrating the Augments MCP Server into an AI workflow is straightforward: a Claude Code assistant can invoke one of nine dedicated MCP tools—such as , , or —to fetch, filter, and format documentation on demand. The server’s structured JSON responses fit neatly into the assistant’s knowledge graph, enabling dynamic code completion, error correction, and documentation generation without manual lookup. This tight coupling between AI and up‑to‑date technical references empowers developers to iterate faster, reduce errors, and stay aligned with the latest ecosystem standards.