Govee MCP Server is an MCP (Model Context Protocol) server designed to control Govee LED devices through the Govee API. It provides tools for turning devices on/off, setting colors via RGB values, and adjusting brightness levels. The server can be used with Cline or other MCP clients and includes a CLI for direct device control.
The Govee MCP Server allows users to automate and control Govee LED devices programmatically via the Govee API. This enables seamless integration of smart lighting into various workflows, whether for home automation, testing environments, or custom applications.
Developers, tech enthusiasts, and home automation users who want to integrate Govee LED devices into their systems or workflows will benefit from this tool. It’s especially useful for those familiar with Python-based development and command-line interfaces.
Govee MCP Server can be installed either automatically via Smithery or manually using pip. It requires environment variables such as API key, device ID, and SKU, which are obtained from the Govee Developer Portal and the Govee Home app.
You should use Govee MCP Server when you need to control Govee LED devices programmatically, either through an MCP client or directly via the CLI. It's ideal for scenarios involving automation, testing, or creating customized lighting setups.
To get started, create a `.env` file in the root directory with your `GOVEE_API_KEY`, `GOVEE_DEVICE_ID`, and `GOVEE_SKU`. Then, install the server using `pip install .` or via Smithery with `npx -y @smithery/cli install @mathd/govee_mcp_server --client claude`. Use the CLI commands like `govee-cli power on` or `govee-cli color 255 0 0` to control your devices.
The server offers three main tools: `turn_on_off` to toggle the LED on/off, `set_color` to define colors using RGB values, and `set_brightness` to adjust the brightness level between 0 and 100.
Install test dependencies with `pip install -e "[test]"`, then run all tests using `pytest tests/`. You can also run specific test files like `pytest tests/test_server.py` for mocked API calls or `pytest tests/test_cli.py` for real-world API interactions. Be cautious as CLI tests execute actual API calls to your device.
Yes! The project structure includes source code under `src/govee_mcp_server/` where you can modify the implementation of the server (`server.py`) or CLI (`cli.py`). Ensure proper test coverage by adding new tests in the `tests/` directory.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.