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.
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Basic information
A Simple MCP Demo With Client & Server
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"hello-mcp": {
"command": "uv",
"args": [
"venv",
"#",
"Create",
"virtual",
"environment"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Hello-MCP π 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.
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.
@tool decorator)MCP standardizes AI model interaction protocols, provides unified tool calling specifications, simplifies complex AI system integration, and enables modular, reusable model capabilities.
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.
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.
Add a new function in `server.py`, decorate it with `@tool`, define the input parameter schema, and test it by calling the client.
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.

Model Context Protocol Servers
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Hindsight: Agent Memory That Learns
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
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