A Model Context Protocol (MCP) server implementation for ESP32-CAM that enables integration with a Large Language Model (LLM). The LLM connects using this library to the ESP32-CAM offering remote camera control, LED management, and system monitoring through standardized MCP tools offering AI capabilities.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"esp32-cam-ai": {
"command": "<see-readme>",
"args": []
}
}
}Are you the author?
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Transform your ESP32-CAM into a powerful, remotely controllable AI-enabled camera system!
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Transform your ESP32-CAM into a powerful, remotely controllable AI-enabled camera system!
This project transforms an ESP32-CAM into a remotely controllable MCP server that can capture images, control LEDs, manage flash lighting, and provide system diagnostics. The server exposes these capabilities through the Model Context Protocol, making it easy to integrate with AI assistants and automation systems.
Brief: Use Copilot or other AI digital assistants, like AI-Toolkit (in VSCode), Home Assistant (HA) or Node-Red, to use your ESP32-CAM, getting information (camera, wifi- or system state) or set GPIO's (led, flash)
notifications/initializedImportant: For optimal image capture, position the ESP32-CAM with the flash LED facing downward. This orientation:
The camera lens should face the subject while the small flash LED (usually next to the lens) points downward toward the surface or subject being photographed.
ArduinoJson - JSON parsing and generationESP32 Camera - Camera functionality (present in ESP32 SDK)Base64 - Image encoding (present in Arduino core)git clone https://github.com/yourusername/esp32-cam-ai.git
cd esp32-cam-ai
Create a .env file in the project root directory with your WiFi credentials:
WIFI_SSID=YourWiFiNetwork
WIFI_PASSWORD=YourPassword
Important: The .env file is required for the project to build successfully. The build system automatically reads these credentials and passes them to the fi