Quick MCP Example
Short and sweet example MCP server / client implementation for Tools, Resources and Prompts.
What is Quick MCP Example?
Quick MCP Example is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to short and sweet example mcp server / client implementation for tools, resources and prompts.
Short and sweet example MCP server / client implementation for Tools, Resources and Prompts.
This server falls under the Developer Tools category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Short and sweet example MCP server / client implementation f
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx quick-mcp-exampleConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Quick MCP Example
Quick MCP Example is a concise Python project that demonstrates all three core MCP server primitives — Tools, Resources, and Prompts — in a single cohesive implementation. It builds a simple knowledge-base chatbot where the LLM can call a tool to query a ChromaDB vector database for RAG responses, select pre-loaded resources for context, and invoke standardized prompt templates for analytical workflows. It is the ideal starting point for developers who want to understand MCP server architecture before building their own integration.
Prerequisites
- Python 3.10 or higher
- uv package manager (recommended) or pip
- An MCP-compatible client or the ability to run the included CLI client (client.py)
- An LLM API key compatible with the client (see client.py for the model used)
Clone the repository
Clone the quick-mcp-example repository to your local machine.
git clone https://github.com/ALucek/quick-mcp-example.git
cd quick-mcp-exampleCreate the ChromaDB vector database
Before running the server, you must populate the vector database. Open MCP_setup.ipynb in Jupyter and follow the instructions to embed a PDF document into ChromaDB.
# Install Jupyter if needed, then:
jupyter notebook MCP_setup.ipynbCreate a virtual environment and install dependencies
Use uv (recommended) or pip to create an isolated environment and install the project's dependencies.
uv venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
uv syncRun the server with the included CLI client
The project ships with a simple CLI client (client.py) that connects to the MCP server and demonstrates tool calls, resource access, and prompt invocation.
python client.py mcp_server.pyExplore the server components in code
Examine mcp_server.py to see how Tools (vector DB query), Resources (static context files), and Prompts (analytical templates) are defined. This is the reference implementation for building your own MCP server.
Quick MCP Example Examples
Client configuration
If you want to connect this example server to Claude Desktop instead of the bundled CLI client, use this configuration. The server is launched by passing mcp_server.py to Python.
{
"mcpServers": {
"quick-mcp-example": {
"command": "python",
"args": ["/absolute/path/to/quick-mcp-example/mcp_server.py"]
}
}
}Prompts to try
Sample interactions that exercise the three MCP primitives — tool (RAG query), resource (context selection), and prompt (template invocation).
- "Search the knowledge base for information about neural network architectures."
- "Use the available resources to give me context on the document collection."
- "Run the analytical prompt template to summarize key themes in the embedded PDF."
- "What tools does this MCP server expose?"Troubleshooting Quick MCP Example
ChromaDB query returns no results
The vector database must be populated before starting the server. Run through MCP_setup.ipynb completely, including the embedding step, and confirm the ChromaDB collection was created successfully.
'ModuleNotFoundError' when running client.py or mcp_server.py
Activate the virtual environment ('source .venv/bin/activate') and run 'uv sync' to install all dependencies. Do not use the system Python interpreter.
The CLI client connects but the LLM returns generic responses without using tools
Check that your LLM API key is correctly set (the client.py file shows which environment variable is expected) and that the model being used supports tool/function calling.
Frequently Asked Questions about Quick MCP Example
What is Quick MCP Example?
Quick MCP Example is a Model Context Protocol (MCP) server that short and sweet example mcp server / client implementation for tools, resources and prompts. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Quick MCP Example?
Follow the installation instructions on the Quick MCP Example GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Quick MCP Example?
Quick MCP Example works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Quick MCP Example free to use?
Yes, Quick MCP Example is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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