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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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

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.

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.

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.

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.

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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