Quick Example

Name: Quick Example
Author: justmywyw

@justmywyw

Repository

About Quick Example

No overview available yet

#mcp #llm #standardization

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "quick-example": {
      "command": "npx",
      "args": [
        "-y",
        "mcprouter"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Quick Example?

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.

How to use Quick Example?

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.

Key features of Quick Example

Demonstrates tools, resources, and prompts in a single server
Integrates a vector database for RAG-style queries
Allows users to choose existing resources for context
Provides reusable prompt templates for analytical workflows
Includes a simple CLI client for testing interaction patterns

Use cases of Quick Example

Learning how to implement MCP server components (tools, resources, prompts)
Prototyping a knowledgebase chatbot with retrieval augmented generation
Testing MCP client-server communication patterns

FAQ from Quick Example

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).

What are the runtime dependencies?

The server requires a Python virtual environment, dependencies installed via uv sync, and a pre-built ChromaDB vector database (created from the provided notebook).

Where does the vector database data live?

The ChromaDB database is created locally by following the instructions in MCP_setup.ipynb—no external data storage is used by default.

What transport or authentication does the server use?

The server uses standard MCP protocol communication; the example client in client.py connects via a subprocess (stdio transport). No authentication is described.

How does this example differ from production MCP servers?

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.