What components does the Quick Example server include?
The server includes tools (functions the LLM can invoke, like querying a vector database), resources (data sources identified by URIs), and prompts (reusable templates for common tasks).
@justmywyw
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使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"quick-example": {
"command": "npx",
"args": [
"-y",
"mcprouter"
]
}
}
}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。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
Quick Example is a sample MCP (Model Context Protocol) server that demonstrates the core MCP capabilities—tools, resources, and prompts—by building a simple knowledgebase chatbot flow. It is intended for developers learning how to create and integrate MCP servers with LLM applications.
Clone the repository, create a ChromaDB vector database as described in the provided Jupyter notebook, set up a virtual environment (recommended with uv), install dependencies via uv sync, then run python client.py mcp_server.py to start both the client and server.
The server includes tools (functions the LLM can invoke, like querying a vector database), resources (data sources identified by URIs), and prompts (reusable templates for common tasks).
The server requires a Python virtual environment, dependencies installed via `uv sync`, and a pre-built ChromaDB vector database (created from the provided notebook).
The ChromaDB database is created locally by following the instructions in `MCP_setup.ipynb`—no external data storage is used by default.
The server uses standard MCP protocol communication; the example client in `client.py` connects via a subprocess (stdio transport). No authentication is described.
This is a learning example, not a production-ready server. It focuses on illustrating the three core MCP components (tools, resources, prompts) in a simple knowledgebase scenario.

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