MCP-RAG Server

Overview

The Mcp Rag server brings Retrieval‑Augmented Generation (RAG) capabilities directly into the MCP ecosystem, enabling AI assistants to answer questions with up‑to‑date, domain‑specific knowledge. Rather than relying on a static prompt or an LLM’s internal memory alone, this server fetches relevant documents from a vector store (ChromaDB) and injects them into the assistant’s context, producing responses that are both accurate and grounded in the latest information.

At its core, the server exposes a single tool. When invoked by an AI client—such as Claude Desktop, Cursor, or any IDE with MCP support—the tool accepts a natural‑language query, performs a vector similarity search against the embedded document collection, and returns the most pertinent passages. These passages are then wrapped into a context‑aware prompt that is sent to the configured LLM (e.g., OpenAI). The resulting generation benefits from two layers of intelligence: the retrieval step ensures factual relevance, while the LLM layer provides fluent, human‑like prose.

Key features include:

• Vector search with ChromaDB – fast, scalable retrieval that scales from a few hundred documents to millions without sacrificing latency.
• Context‑aware prompt construction – automatically formats retrieved snippets into a structured prompt that preserves source attribution and encourages the LLM to reference them.
• Seamless MCP integration – the server follows the standard MCP protocol, allowing any compliant client to call with minimal configuration.
• Environment‑driven LLM selection – by setting the (or other provider variables), developers can switch between different LLM backends without code changes.

Typical use cases span knowledge‑base bots, internal support assistants, and research tools where up‑to‑date data is essential. For example, a company can host an MCP Rag server that indexes its policy documents; employees then ask questions via their preferred IDE, and the assistant returns precise answers pulled from the latest policies. Similarly, academic teams can feed research papers into ChromaDB and let AI generate literature reviews that cite specific studies.

Because the server is lightweight and fully open source, developers can deploy it in a private environment or scale it behind a Kubernetes cluster. Its modular architecture—separate modules for retrieval, context management, LLM communication, and prompt building—makes it easy to extend or replace components (e.g., swap ChromaDB for Pinecone). This flexibility, combined with the out‑of‑the‑box MCP compatibility, gives developers a powerful yet straightforward way to embed RAG into their AI workflows.