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.
MCP Knowledge Base Assistant
@kofiadom
基本信息
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 Assistant 配置
MCP Knowledge Base Assistant 工具
工具列表暂未提供
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
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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