MCP Knowledge Base

@gmogmzGithub

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

gmogmzGithub

关于 MCP Knowledge Base

A lightweight knowledge base assistant using MCP with LLM integration. Features a streamlined server-client architecture combining custom tools with a knowledge base, all accessible via SSE transport. Ideal for building simple AI-powered knowledge assistants.

MCP Knowledge Base 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 工具

工具列表暂未提供

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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?

A simple MCP client‑server application that uses OpenAI to answer questions from a built‑in knowledge base. It is intended for learning the MCP protocol or building a custom Q&A system.

How to use MCP Knowledge Base?

Install dependencies with Poetry, set your OpenAI API key in a .env file, start the server via poetry run python server.py, and run the client with poetry run python client-sse.py. The client offers two modes: direct tool calls (uncomment the test line) or LLM‑powered interactions that interpret natural‑language queries.

Key features of MCP Knowledge Base

MCP server exposing tools via decorators
MCP client with LLM query interpretation
Knowledge base stored in data/kb.json
Direct tool‑call mode for testing
LLM‑powered mode for natural‑language questions

Use cases of MCP Knowledge Base

Learn the MCP client‑server architecture interactively
Build a domain‑specific Q&A system with a custom knowledge base
Prototype tool calls with or without an LLM orchestrator

常见问题

What are the runtime requirements?

Python 3.9 or higher, Poetry for dependency management, and a valid OpenAI API key.

How do I change the knowledge base content?

Edit the `data/kb.json` file with the Q&A pairs you want to use.

How do I add a new server tool?

Define a new Python function decorated with `@mcp.tool()` inside `server.py`.

How do I run the client without an LLM?

Uncomment the `asyncio.run(test_direct_tool_calls())` line in `client-sse.py` to call tools directly.

How do I change the LLM model used?

Modify the `model` parameter in the `MCPClient` initialization inside `client-sse.py`.

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