Centralize ESP32 / ESP-IDF commands behind an MCP server so LLM-powered agents can build, clean and flash firmware via simple tool calls.
https://github.com/horw/esp-mcpStop context-switching between your AI assistant and terminal every time you need to build, flash, or debug ESP32 firmware. ESP-MCP bridges that gap by exposing ESP-IDF commands through the Model Context Protocol, letting your AI handle the heavy lifting while you focus on the actual code.
You're deep in conversation with Claude or GPT about your ESP32 project. They're helping you debug WiFi connectivity, suggesting code improvements, even writing entire modules. Then you need to test the changes, and suddenly you're back to manually running idf.py build, idf.py flash, parsing error logs, and explaining the results back to your AI.
That friction kills momentum. ESP-MCP eliminates it entirely.
With ESP-MCP running, your development conversations become seamless:
"Build this project and flash it to my ESP32 on port /dev/ttyUSB0"
"Clean the build artifacts and rebuild with debugging enabled"
"The build failed - can you analyze the error and suggest a fix?"
Your AI assistant executes these operations directly, analyzes the output, and continues the conversation without missing a beat. No copy-pasting terminal commands or explaining error logs.
Core ESP-IDF Operations: Build, clean, flash, and monitor commands work out of the box. Your AI can handle the entire build-deploy cycle without your intervention.
Intelligent Error Handling: The experimental auto-fix feature means your AI doesn't just report build failures - it analyzes the logs and suggests concrete solutions. Compile error? Missing dependency? Your assistant spots the issue and knows how to fix it.
Multi-Project Management: Working on several ESP32 projects? Your AI can switch between project directories and manage builds across your entire workspace.
Here's where ESP-MCP shines in practice:
Rapid Prototyping: "Take this sensor reading code, build it for ESP32-S3, and flash it to my dev board." Your AI handles the toolchain complexity while you iterate on functionality.
Debugging Sessions: When builds fail, your AI can immediately retry with different configurations, analyze linker errors, or suggest dependency fixes - all without you leaving the conversation.
Learning ESP32: New to ESP-IDF? Your AI becomes a hands-on tutor, explaining concepts while actually building and flashing examples to your hardware.
Clone the repo, point it at your ESP-IDF installation, and configure your AI client. The MCP protocol handles the rest - no complex APIs to learn or authentication to configure.
git clone https://github.com/horw/esp-mcp.git
# Configure with your ESP-IDF path
# Add to your AI client's MCP servers
Your AI immediately gains access to your entire ESP32 development environment.
While ESP-MCP handles essential build operations today, the roadmap points toward something bigger: comprehensive embedded development assistance. Future versions aim to integrate device management, real-time monitoring, and even home automation workflows.
This isn't just about running commands - it's about creating an AI-native development experience for embedded systems.
The 66 stars and active development tell the story: developers want their AI assistants to understand their hardware workflow. ESP-MCP delivers that integration without forcing you to change how you work.
Whether you're building IoT prototypes, commercial products, or just experimenting with ESP32 capabilities, ESP-MCP turns your AI from a code assistant into a complete development partner.
Ready to streamline your ESP32 workflow? The setup takes five minutes, but the productivity gains compound with every conversation.