MCP Knowledge Base Assistant

The MCP Knowledge Base Assistant addresses a common challenge in modern AI‑powered applications: enabling an assistant to answer domain‑specific questions with authoritative, up‑to‑date information. Rather than hard‑coding policy text or relying on the assistant’s internal knowledge, this server exposes a structured knowledge base as an MCP tool. The OpenAI model can then request the relevant data, ensuring that responses are accurate, consistent, and easy to maintain.

At its core, the server hosts a JSON file that contains question‑answer pairs about company policies. Through MCP, it offers a single tool——which returns the entire knowledge base as a formatted string. The client, written in Python, connects to this server and forwards user queries to the OpenAI API. The model interprets natural language questions, decides whether it needs policy information, and calls the MCP tool when appropriate. This pattern keeps the assistant’s logic clean while delegating data retrieval to a dedicated service.

Key capabilities include:

• Tool exposure: The server presents the knowledge base as an MCP tool, allowing fine‑grained control over what data can be accessed.
• Natural language interface: Users interact with the assistant in plain English; the OpenAI model translates intent into tool calls.
• Containerization: A Dockerfile is provided so the server can run in a reproducible, isolated environment—ideal for CI/CD pipelines or cloud deployment.
• Extensibility: Adding new policy topics simply involves updating the JSON file; no code changes are required.

Typical use cases span internal help desks, compliance checkers, and onboarding assistants. For example, a new employee can ask, “What is the company’s equal‑opportunity policy?” and receive a concise, verifiable answer. In regulated industries, the server can be updated by compliance teams and instantly reflected in all assistant interactions without retraining models.

By integrating MCP with an OpenAI client, developers gain a powerful workflow: the model focuses on language understanding and generation, while the MCP server guarantees reliable data access. This separation of concerns reduces maintenance overhead, improves answer accuracy, and aligns with best practices for building trustworthy AI assistants.