Implementing RAG Server Using MCP
Explore how to implement a complete retrieval-augmented generation (RAG) server with MCP. Learn to ingest and vectorize documents using ChromaDB, enable semantic search, and empower an AI agent to answer queries with private, company-specific knowledge.
Our agent can now use tools to interact with live web services. Some tasks, however, require information that is not available on the public web. LLMs can provide broad general knowledge, but they can't access an organization’s internal documents, project plans, or proprietary data unless you provide that context. This lesson addresses that limitation by building a retrieval-augmented generation (RAG) pipeline that retrieves relevant content from a defined document collection and provides it as context to the model. We will expose this retrieval capability through a dedicated MCP server.
Giving our agent a knowledge base
Tool access gives an agent access to external capabilities, but it does not guarantee access to private organizational data. If we ask about an internal vacation policy or the procedure for requesting new hardware, the agent may not be able to answer reliably unless that information is provided through an authorized data source. To address this limitation, we need a way to retrieve relevant internal information. We can connect the agent to a searchable internal knowledge base that supplies relevant context for answering questions about internal documents and policies. The following use case shows how this retrieval pattern works.
Scenario: Building a corporate document assistant
A new employee may have many questions about company policies and procedures, such as “What is the policy on remote work?”, “How many vacation days do I get?”, and “What are the company’s core values?” Answering these questions manually can take time from HR staff and team leads. The company maintains an employee_handbook.txt file containing this information. Here is an excerpt from employee_handbook.txt:
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 the specific data we provide.
For our document assistant, this workflow consists of two primary stages that happen every time a user asks a question: ...