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MCP Knowledge Base Assistant

@kofiadom

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

kofiadom

关于 MCP Knowledge Base Assistant

An intro to MCP: an MCP server with a knowledge base tool containerized with Docker and connected to a client-side python application using SSE

#mcp#knowledge-base#ai-assistant

MCP Knowledge Base Assistant 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

MCP Knowledge Base Assistant 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Knowledge Base Assistant?

MCP Knowledge Base Assistant is a demonstration of the Model Context Protocol (MCP) that connects an OpenAI-powered client to a knowledge base server. It shows how to build an AI assistant that answers questions about company policies by accessing a knowledge base through MCP.

How to use MCP Knowledge Base Assistant?

Install prerequisites (Python 3.11+, OpenAI API key), clone the repo, create a virtual environment, install dependencies, and set OPENAI_API_KEY in a .env file. Run the server with python server.py (or Docker), then run python client.py in another terminal. The client sends a sample query; modify client.py to ask different questions.

Key features of MCP Knowledge Base Assistant

  • Exposes a knowledge base as an MCP tool (get_knowledge_base)
  • Integrates OpenAI GPT-4o for natural language query processing
  • Supports both direct Python execution and Docker containerization
  • Proper lifecycle management with async context managers
  • Simple JSON-based knowledge base (data/kb.json) for easy customization
  • Follows MCP client-host-server architecture with SSE transport on port 8050

Use cases of MCP Knowledge Base Assistant

  • Answering employee questions about company policies (e.g., equal opportunity)
  • Demonstrating how to build and connect an MCP server to an LLM
  • Testing MCP tool integration with a minimal knowledge base
  • Extending the server with additional tools and policies

常见问题

1

What does the server tool do?

The server exposes a single tool called `get_knowledge_base` that retrieves the entire knowledge base from `data/kb.json` as a formatted string.

2

What are the runtime requirements?

Python 3.11 or higher, an OpenAI API key, and the dependencies listed in `requirements.txt`. Docker is optional for containerized server execution.

3

Where does the knowledge base data live?

The knowledge base is stored in `data/kb.json`, a local JSON file containing question-answer pairs about company policies. You can add more pairs to expand it.

4

How does the client handle resources?

The client uses Python async context managers (`async with`) to automatically initialize and clean up all resources when exiting the context, following MCP best practices.

5

What transport does the server use?

The server communicates via SSE (Server-Sent Events) transport on port 8050. Authentication is handled through the OpenAI API key set in the environment.

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