Add API key to .env file MCP Server

@heureux831

Repository

Basic information

About Add API key to .env file MCP Server

A simplest MCP system implemented in Python, including a client and several servers.

#mcp #mcp-demo #python

Add API key to .env file MCP Server config

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

{
  "mcpServers": {
    "mcp-demo-heureux831": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Add API key to .env file MCP Server tools

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Overview

What is Add API key to .env file?

A minimal MCP (Model Context Protocol) project that demonstrates how to set up an MCP server with an API key stored in a .env file. It uses DeepSeek as the LLM backend and includes sample Python and Node.js servers for task lists, weather, and stock data. Aimed at developers learning MCP.

How to use Add API key to .env file?

Create a .env file with OPENAI_API_KEY, OPENAI_BASE_URL, and OPENAI_MODEL (e.g., DeepSeek). Install dependencies with uv add mcp openai python-dotenv. Edit server.json to point to the server scripts (tasklist.py, weather.py, stock.js). Run the client with cd client && uv run client.py server.json.

Key features of Add API key to .env file

Minimal MCP project for learning.
Uses .env file for API key configuration.
Supports multiple LLM services via environment variables.
Includes Python and Node.js server examples.
Client auto‑discovers servers from server.json.
Easy setup with uv virtual environment.

Use cases of Add API key to .env file

Learn how MCP servers and clients interact.
Prototype a multi‑server MCP application.
Test DeepSeek or other OpenAI‑compatible LLMs with MCP.
Quickstart template for custom MCP server development.

Frequently asked questions

What API key is required?

A DeepSeek API key (or any OpenAI‑compatible API) must be added to the `.env` file as `OPENAI_API_KEY`.

How do I switch to a different LLM service?

Change the `OPENAI_BASE_URL` and `OPENAI_MODEL` in `.env` to match the desired service.

What servers are provided?

The project includes `tasklist.py`, `weather.py`, and `stock.js` as example MCP servers.

How do I run the project?

Activate the virtual environment, then run `uv run client.py server.json` from the `client` directory.

Where does the data live?

Task data is stored in `tasks.csv` in the project root. Other server data comes from external APIs (not specified).

Comments

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