FAISS‑Powered MCP RAG Server

Overview

The Probonobonobo Sui MCP Server is a specialized Machine Conversation Protocol (MCP) server that bridges the gap between AI assistants and domain‑specific knowledge bases. By integrating a FAISS vector store with a GitHub‑centric workflow, it enables AI agents to perform Retrieval‑Augmented Generation (RAG) on Sui Move code, providing accurate, context‑rich responses that reflect the latest state of the repository ecosystem.

This server solves a common pain point for developers building AI‑powered tooling around blockchain smart contracts: the difficulty of quickly locating relevant code snippets or documentation in a rapidly evolving codebase. Traditional search approaches return raw files, leaving the AI to parse and interpret them manually. The MCP server automates this process by indexing Move files, creating dense embeddings for each chunk, and exposing a lightweight API that returns the most semantically similar documents in response to natural‑language queries. The result is a seamless RAG loop where the LLM can ask, retrieve, and synthesize information without manual curation.

Key capabilities include:

• FAISS‑backed vector search that scales to thousands of Move files while maintaining low latency.
• Automated GitHub search and indexing: a CLI tool that queries repositories, extracts files, chunks them, generates embeddings, and rebuilds the index in one command.
• RAG orchestration: a single endpoint that accepts a user query, retrieves top‑k documents, and forwards the combined prompt to an LLM (OpenAI or a simulated backend) for answer generation.
• Rich client examples that demonstrate end‑to‑end usage, from downloading source code to querying the server.
• Configurable parameters (e.g., , output format) that let developers tailor the response size and detail to their workflow.

Real‑world scenarios where this server shines include:

• Smart contract debugging: An AI assistant can answer questions about a specific function or module by pulling in the exact source code context.
• Documentation generation: Developers can ask for explanations of complex Move concepts and receive concise, code‑referenced answers.
• Onboarding new contributors: New team members can query the knowledge base to understand patterns and conventions without sifting through thousands of files.
• Continuous learning: As the codebase evolves, re‑indexing keeps the vector store up to date, ensuring that AI responses reflect the latest state.

Integration into existing AI workflows is straightforward. The MCP server exposes standard HTTP endpoints that any AI assistant (Claude, GPT‑4o, etc.) can call using the Model Context Protocol. By treating the server as a “tool” within the MCP ecosystem, developers can embed advanced code retrieval directly into conversational agents, enabling more accurate, context‑aware interactions without custom parser logic. The server’s modular design also allows swapping out the underlying vector store or embedding model, giving teams flexibility to adapt to new requirements.

In summary, the Probonobonobo Sui MCP Server delivers a ready‑made, production‑grade RAG pipeline for Sui Move codebases. It eliminates manual search and parsing overhead, empowers AI assistants with precise, up‑to‑date knowledge, and fits naturally into modern developer toolchains that rely on the MCP standard.