Hello-MCP ๐
@Cookie-HOO
ๅ ณไบ Hello-MCP ๐
A Simple MCP Demo With Client & Server
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{
"mcpServers": {
"hello-mcp": {
"command": "uv",
"args": [
"venv",
"#",
"Create",
"virtual",
"environment"
]
}
}
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ๅฏไปฅๅจ ## Tools ๆ ้ขไธๅๅบๅทฅๅ
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What is Hello-MCP ๐?
Hello-MCP ๐ is a simple implementation of an MCP (Model Control Protocol) client and server. It helps developers understand what the MCP protocol is and how it works by providing hands-on experience in building and using both client and server components.
How to use Hello-MCP ๐?
Clone the repository, install uv (e.g., via curl -LsSf https://astral.sh/uv/install.sh | sh on macOS), create a virtual environment (uv venv), install dependencies (uv sync), and activate it. Configure your DeepSeek API key in config.yaml (copy from config.example.yaml). Run the client in stdio mode with python -m hello_mcp.client2stdio --server-path ./hello_mcp/server.py or in SSE mode by first starting the server (python -m hello_mcp.server --transport sse) then the client (python -m hello_mcp.client2sse --server-url http://127.0.0.1:8000). Debug the server using uv run mcp dev hello_mcp/server.py.
Key features of Hello-MCP ๐
- Supports registering MCP servers via SSE and stdio transports
- Interactive command-line chat interface
- Integrates with DeepSeek API (configurable API key)
- Streamed response output
- Simple server built on FastMCP with a path utility tool
- Health check endpoint on the server
Use cases of Hello-MCP ๐
- Learning the MCP protocol by building your own client and server
- Gaining hands-on experience with MCP tool registration and invocation
- Debugging MCP servers using the built-in MCP Inspector
- Experimenting with MCP in daily AI workflows (e.g., with Claude Desktop or Cursor)
- Extending the server with custom tools (by adding functions with the
@tooldecorator)
FAQ from Hello-MCP ๐
What problems does MCP solve?
MCP standardizes AI model interaction protocols, provides unified tool calling specifications, simplifies complex AI system integration, and enables modular, reusable model capabilities.
How do I set up the DeepSeek API key?
Copy config.example.yaml to config.yaml and fill in your DeepSeek API key (obtained from https://platform.deepseek.com/api_keys). Ensure no extra spaces or quotes around the key.
What if the port is already in use when starting the server?
Use lsof -i :8000 to find the process using the port, terminate it, or change the server port and update the client registration URL accordingly.
How can I extend Hello-MCP with new tools?
Add a new function in server.py, decorate it with @tool, define the input parameter schema, and test it by calling the client.
What are the runtime requirements?
Python 3, uv for project management, and a DeepSeek API key (currently the only supported AI provider). No additional MCP hosts are required; the server runs as a standalone Python process.
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