Overview

The MCP Knowledge Base server is a lightweight Model Context Protocol (MCP) implementation that bridges an external knowledge repository with AI assistants such as Claude or OpenAI’s GPT models. By exposing a set of tools that query a JSON‑encoded knowledge base, the server enables conversational agents to answer domain‑specific questions without relying on a large, constantly retrained model. This approach delivers fast, deterministic responses and keeps sensitive or proprietary data out of the public AI service.

What Problem Does It Solve?

Many developers need a way to let an LLM answer questions about internal documentation, FAQs, or support knowledge bases without exposing that data to the cloud provider. The MCP Knowledge Base server solves this by hosting a small, self‑contained API that the LLM can call during a conversation. It removes the need for expensive fine‑tuning or custom embeddings, while still allowing the LLM to interpret natural language and decide which tool to invoke. The result is a secure, cost‑effective workflow that keeps the heavy lifting on the client side and only uses the cloud model for language understanding.

How It Works

The server loads a JSON file () containing question‑answer pairs and registers a tool that the MCP client can call. When an AI assistant receives a user query, it parses the intent and calls the appropriate tool via MCP. The tool returns a structured response that the LLM incorporates into its final reply. Because the knowledge base is static, look‑ups are instantaneous and deterministic, providing consistent answers across sessions. Developers can extend the server by adding new tool functions decorated with or by updating the JSON file, making the system highly adaptable to evolving knowledge.

Key Features

• Tool‑based querying: Exposes a single, well‑defined tool that searches the knowledge base and returns matching answers.
• Easy customization: Add new tools or modify the data source without touching the core server logic.
• LLM‑agnostic: Works with any MCP‑compatible client, whether it’s OpenAI’s GPT or Anthropic’s Claude.
• Secure data handling: Keeps the knowledge base local, eliminating the need to send proprietary content to third‑party APIs.
• SSE support: The client example demonstrates Server‑Sent Events, allowing real‑time streaming of responses for a more interactive experience.

Use Cases

• Internal help desks: Provide instant answers to employee questions about policies, onboarding procedures, or software usage.
• Customer support bots: Deliver consistent FAQ responses while still leveraging the conversational abilities of an LLM.
• Educational assistants: Offer quick references to curriculum material or textbook excerpts without exposing the entire syllabus.
• Compliance checks: Ensure that AI outputs adhere to company guidelines by referencing a curated knowledge base.

Integration into AI Workflows

Developers can plug this server into their existing MCP pipelines with minimal effort. The client example shows two modes: a direct tool‑call mode for testing and an LLM‑powered mode that interprets natural language before invoking the tool. By simply changing the parameter, teams can switch between different LLM providers without modifying the server. The modular design means that any MCP‑compatible client—be it a custom web interface, a Slack bot, or a voice assistant—can consume the knowledge base with the same ease.

The MCP Knowledge Base server exemplifies how a focused, tool‑centric approach can enhance AI assistants with reliable, domain‑specific knowledge while keeping data secure and operations lightweight.