Implementing RAG Server Using MCP
Learn how to build a RAG server with MCP, LangChain, and ChromaDB, allowing an agent to answer questions from a knowledge base.
We have successfully taught our agent to use tools that interact with live web services, but what happens when the information it needs isn’t on the public web? LLMs have vast general knowledge, yet they are completely unaware of your company’s internal documents, project plans, or proprietary data. This lesson tackles that fundamental challenge by building a complete retrieval-augmented generation (RAG) pipeline, a system that allows an agent to find and read from specific documents, and exposing this powerful capability as a self-contained MCP server.
Giving our agent a knowledge base
An agent’s ability to use external tools is a powerful starting point, but it’s fundamentally limited by the public nature of most APIs. If we ask it about our company’s internal vacation policy or the procedure for requesting new hardware, it would fail, as this private knowledge wasn’t part of its training data. To address this limitation, we must move beyond basic tool invocation and equip our agent with a dedicated private library, a searchable knowledge base that it can read from to answer questions with greater accuracy and context-awareness.To understand how we can achieve this, let’s consider a more advanced use case.
Scenario: Building a corporate document assistant
Imagine a new employee joins our company and has dozens of questions: “What is the policy on remote work?” “How many vacation days do I get?”, “What are the company’s core values?”. Answering these manually consumes valuable time from HR and team leads. The company has a comprehensive employee_handbook.txt file for this purpose. Here is a glimpse of what the employee handbook file looks like:
You can download the employee_handbook.txt file by clicking the download button in the widget above.
Expecting new hires to read it cover-to-cover is unrealistic. Our objective is to build an intelligent assistant that acts as an expert on this handbook. When an employee asks a question, the agent must not use its general, pretrained knowledge. Instead, it must find the most relevant section within the handbook and use only that information to construct its answer. The technical solution for this challenge is a powerful technique known as retrieval-augmented generation (RAG), which ensures responses are always accurate, verifiable, and grounded in official company policy.
A quick look at the RAG workflow
Fundamentally, retrieval-augmented generation (RAG) is a technique used to make LLM responses more reliable and fact-based by connecting them to an external knowledge source. Instead of letting the LLM answer from its generalized pretrained knowledge, a RAG system first fetches relevant information and provides it to the LLM as direct context for generating a response. This process dramatically reduces hallucinations and ensures the answers are grounded in ...