Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-server-demo List the available tools and resources this server exposes."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This repository provides an educational example of a Model Context Protocol (MCP) server implemented in Python using the FastMCP library. It demonstrates how to expose tools, resources, and prompts to AI clients, enabling seamless integration with applications like IDEs, chatbots, and agent frameworks.
Related MCP server: FastMCP Example Server
Model Context Protocol (MCP) is an open protocol that standardizes how AI applications connect to external tools and data sources. MCP servers expose:
Tools: Executable functions that can be called by AI clients
Resources: Data sources for context (files, APIs, etc.)
Prompts: Reusable templates for interactions
Learn more: modelcontextprotocol.io
FastMCP is a Python library for building MCP servers quickly and easily. It provides:
Simple API for defining tools, resources, and prompts
Support for stdio and HTTP transports
Type-safe schemas for tool inputs/outputs
Integration with popular Python frameworks
MCP uses a client-server architecture:
Host: The AI application (e.g., VS Code, Claude Desktop)
Client: Connects to one or more MCP servers
Server: Exposes tools, resources, and prompts
Servers declare their capabilities during initialization. Tools are listed and can be invoked by the client or model. FastMCP makes it easy to implement these features in Python.
Define Python functions as MCP tools
Expose resources (e.g., files, API data)
Add prompts for structured interactions
Support for both stdio and HTTP transports
Install FastMCP:
pip install fastmcpRun the example server:
python mcp_server.pyConnect an MCP-compatible client (e.g., Claude Desktop, VS Code, etc.) to the server.
See mcp_server.py for example code.
Note: This example is for educational purposes. Always review server code and tool definitions before connecting to any AI application.
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