The embedded systems industry is entering a new phase where AI is becoming part of firmware development, hardware debugging, PCB design and testing. With the Model Context Protocol (MCP), AI assistants can do more than just generate code. They can interact with development tools, communicate with microcontrollers, execute build commands, inspect debugging information and even analyse signals from physical hardware.
Imagine asking an AI assistant to build your ESP32 firmware, flash it onto your development board, read UART logs, identify an error and suggest a fix. Instead of manually switching between multiple tools, you can connect these capabilities through MCP servers.
MCP stands for Model Context Protocol. It is an open standard that allows AI applications such as Claude, Cursor and other compatible clients to communicate with external tools and services. An MCP server exposes specific capabilities to an AI assistant, allowing it to interact with software, hardware and development environments.
For embedded engineers, this opens up interesting possibilities across microcontrollers, RTOS, firmware development, PCB design, debugging and hardware testing.
Here are 10 interesting open-source MCP projects worth exploring.
1. ESP-MCP – AI-powered ESP32 development
If you work with ESP32 microcontrollers and ESP-IDF, ESP-MCP is an interesting project to explore.
Normally, when developing an ESP32 application, you need to perform several tasks manually. You create an ESP-IDF project, configure the target chip, write firmware, execute build commands, flash the application and test the output using a serial monitor.
ESP-MCP brings several of these operations into an AI-assisted workflow.
It provides tools for installing ESP-IDF dependencies, creating ESP32 projects, selecting target chips, building firmware, listing serial ports, flashing applications and running tests.
For example, you could ask an MCP-compatible AI assistant to create a new ESP32-S3 project, configure the target, build the firmware and flash it onto your connected development board.
The AI assistant can call the appropriate tools instead of simply explaining which terminal commands you need to execute.
Practical applications:
- ESP32 and ESP32-S3 firmware development.
- ESP-IDF project creation and configuration.
- Firmware compilation and flashing.
- Automated firmware testing.
- Faster prototyping of IoT applications.
This repository describes itself as a proof-of-concept project, so check its supported commands and current limitations before using it in production.
Link: https://github.com/horw/esp-mcp
2. PlatformIO MCP – Automate embedded firmware development
PlatformIO is widely used by embedded developers working with Arduino, ESP32, STM32 and other microcontroller platforms.
The PlatformIO MCP project connects AI coding agents with the PlatformIO development environment.
It allows an AI assistant to interact with the firmware development workflow, including building projects, uploading firmware, monitoring serial output and running tests.
One particularly interesting capability is the ability to analyse firmware crashes and core dumps. Instead of manually reading large error logs, the AI assistant can use the available tools to investigate the failure and help identify the source of the problem.
It also provides functionality for inspecting firmware size, partition information and other development details.
For example, imagine that you have written firmware for an ESP32-based IoT device. After uploading the application, the board crashes with a Guru Meditation Error. You can ask the AI assistant to inspect the crash information, identify the relevant source location and suggest a correction.
The project also includes configurable safety policies. You can choose a read-only mode, a build-only mode or a full-access mode depending on your development environment.
Practical applications:
- Building and compiling embedded firmware.
- Flashing applications to microcontrollers.
- Monitoring UART output.
- Analysing ESP32 crashes.
- Running embedded unit tests.
- Inspecting firmware size and memory usage.
This is particularly interesting for engineers who use VS Code and PlatformIO for daily development.
Link: https://github.com/powerdragonfire/platformio.mcp
3. Embedded Debugger MCP – AI-assisted hardware debugging
Debugging is one of the most time-consuming activities in embedded software development.
When an application crashes, engineers often need to connect a debugging probe, halt the processor, inspect registers, check memory locations, configure breakpoints and analyse the call stack.
Embedded Debugger MCP aims to connect these debugging operations with AI assistants.
It is a Rust-based MCP server that supports two debugging backends: probe-rs and OpenOCD.
It provides tools for discovering debug probes, connecting to targets, controlling processor execution, reading memory, managing breakpoints, flashing firmware and communicating through RTT.
One of its interesting capabilities is crash diagnosis. It can collect information from processor fault registers and provide structured information that an AI assistant can use to investigate a crash.
For example, if an ARM Cortex-M microcontroller encounters a HardFault, an engineer can use the debugging tools to inspect fault-related registers, processor state and the relevant program counter.
The AI assistant can then help interpret the collected information and investigate possible causes.
It supports workflows involving ARM Cortex-M, RISC-V and supported ESP32 targets, depending on the selected backend and hardware.
Practical applications:
- JTAG and SWD debugging.
- ARM Cortex-M firmware debugging.
- Memory and register inspection.
- Breakpoint management.
- Firmware flashing.
- Crash and fault analysis.
This can be particularly useful for engineers working with STM32, embedded Linux companion processors and other microcontroller-based systems. Hardware compatibility should be checked before connecting your target.
Link: https://github.com/Adancurusul/embedded-debugger-mcp
4. Embedded MCP – Communicate directly with microcontrollers
One of the biggest challenges in embedded development is understanding what is happening inside the physical hardware.
You might write firmware, flash it onto a board and open a serial monitor to understand what the microcontroller is doing.
Embedded MCP makes this interaction available to AI assistants.
It connects an MCP-compatible client to a physical development board using UART or USB serial communication.
The server provides tools to discover serial ports, read device output, send commands, decode register values and identify available firmware flashing utilities.
An interesting feature is register decoding using CMSIS-SVD information.
CMSIS-SVD files describe the peripheral registers of supported microcontrollers. Instead of manually looking up every register field in a datasheet, the server can help decode raw register values into meaningful bit fields.
For example, if your firmware prints a hexadecimal register value, you can ask the AI assistant to interpret the individual bits using the appropriate register description.
It also supports dry-run firmware flashing, allowing you to inspect the command before performing the actual operation.
Practical applications:
- UART communication with STM32 and ESP32.
- Reading firmware logs.
- Sending commands to embedded devices.
- Register decoding.
- Investigating firmware behaviour.
- Integrating flashing utilities into AI workflows.
This is an interesting project for engineers working with low-level drivers, peripheral configuration and microcontroller debugging.
Link: https://github.com/atillab1/embedded-mcp
5. Serial MCP – AI-powered UART and USB communication
UART is one of the most commonly used communication interfaces in embedded systems.
Whether you are working with STM32, ESP32, Arduino, GPS modules, Bluetooth modules or industrial controllers, serial communication is an essential part of development and debugging.
Serial MCP provides an MCP interface for interacting with serial ports.
It allows AI agents to discover connected devices, read incoming serial data, transmit commands and monitor communication.
It also includes capabilities for interpreting different data formats and protocols, including line-based messages, delimiter-based frames, SLIP and COBS.
Additional protocol support includes AT commands, JSON, NMEA-0183 and Modbus ASCII.
Imagine developing an embedded device that communicates with a GPS module over UART. Instead of manually copying serial output into an AI assistant, you can connect the serial interface and ask the assistant to inspect the received messages.
It can help you understand the communication format, identify unexpected responses and investigate potential problems.
Practical applications:
- UART debugging.
- USB-to-serial communication.
- AT command testing.
- GPS and NMEA message analysis.
- Modbus ASCII communication.
- Embedded device monitoring.
This project is useful for engineers who regularly work with serial interfaces and hardware communication protocols.
Link: https://github.com/qarnet/serial-mcp
6. KiCad MCP – AI-assisted PCB design
PCB design is another area where AI can potentially save engineers a significant amount of time.
KiCad is an open-source electronic design automation platform used for schematic capture, PCB layout, component placement and board manufacturing.
The KiCad MCP Server developed by Seeed Studio connects AI assistants with KiCad design workflows.
It provides capabilities for analysing electronic schematics, inspecting PCB layouts, understanding component connections and validating designs.
One interesting feature is netlist tracing. A netlist describes the electrical connections between components in a circuit. An AI assistant can use this information to understand how different components are connected.
It also supports design validation through electrical rule checks and design rule checks.
For example, imagine you are designing a PCB containing an STM32 microcontroller, voltage regulators, sensors and communication interfaces. You could ask an AI assistant to inspect the schematic, identify component connections, analyse pin assignments and help validate the design.
The project also includes capabilities for generating embedded-related outputs such as device tree files and hardware test code.
Practical applications:
- Electronic schematic analysis.
- PCB layout inspection.
- Component and netlist analysis.
- Pin configuration analysis.
- Electrical rule checking.
- PCB design validation.
- Embedded code generation.
For electronics engineers, this is one of the most interesting areas where AI-assisted engineering workflows can develop further.
Link: https://github.com/Seeed-Studio/kicad-mcp-server
7. Zephyr MCP Server – Connect embedded devices with AI
Zephyr is an open-source real-time operating system designed for resource-constrained embedded devices.
It is used in applications involving IoT, industrial electronics, connected devices and microcontroller-based systems.
An interesting development is that Zephyr now includes an experimental MCP server library that allows network-connected Zephyr devices to expose their own tools to AI agents.
Unlike conventional MCP servers that run on a PC and control external software, this implementation allows the embedded device itself to act as an MCP server.
The implementation uses Zephyr’s HTTP server and JSON libraries to expose tools through HTTP.
For example, imagine an IoT device containing an environmental sensor. The device could expose a tool that allows an AI assistant to request sensor readings.
Similarly, a connected embedded device could expose selected functions for controlling an LED, checking device status or retrieving application information.
This creates opportunities for developing AI-connected embedded products.
It is important to understand that the upstream implementation is experimental. Its currently documented functionality is limited, and advanced features such as authorization and session management are still planned.
Practical applications:
- AI-connected IoT devices.
- Remote device monitoring.
- Sensor data access.
- Smart home applications.
- Embedded device control.
- Connected industrial systems.
This is particularly interesting for engineers exploring the combination of embedded systems, IoT and edge AI.
Link: https://github.com/zephyrproject-rtos/zephyr
8. GDB MCP – AI-assisted C and C++ debugging
GDB, or GNU Debugger, is one of the most important debugging tools used in C and C++ development.
Embedded engineers frequently use GDB to inspect variables, examine memory, set breakpoints, step through instructions and investigate application crashes.
GDB MCP connects these debugging capabilities with AI assistants.
It allows an AI agent to interact with debugging sessions through a common tool interface.
The project uses the Debug Adapter Protocol and supports GDB or LLDB backends.
An AI assistant can start a debugging session, set breakpoints, step through source code, inspect variables, evaluate expressions and examine memory.
Consider a situation where your embedded C application crashes because of an unexpected pointer value.
Normally, you would open a debugging session, inspect the relevant variables, check the call stack and investigate the source of the issue.
With GDB MCP, the AI assistant can perform supported debugging operations and use the returned information to help identify the problem.
This does not mean the AI will automatically understand every hardware-specific failure. The debugging results still depend on the target configuration, symbols, debug information and toolchain.
Practical applications:
- C and C++ debugging.
- Variable and register inspection.
- Breakpoint management.
- Call stack analysis.
- Memory inspection.
- Investigating runtime errors.
- Debugging embedded application code.
For engineers working with embedded C, C++ and Linux-based applications, this is worth exploring.
Link: https://github.com/birdeclipse/gdb-mcp
9. Saleae Logic 2 MCP – Analyse digital signals using AI
Hardware debugging is not limited to reading firmware logs. Sometimes, you need to understand what is actually happening on the communication bus.
This is where logic analysers become useful.
Saleae Logic 2 is a popular software platform for capturing and analysing digital signals using compatible logic analyser hardware.
The community-developed Saleae Logic 2 MCP Server connects this environment with AI assistants.
It provides capabilities for capturing digital signals, decoding communication protocols, measuring signal timing and exporting captured data.
For example, imagine that your STM32 microcontroller is communicating with an external sensor using I2C.
The sensor is not responding correctly, and you suspect a communication timing issue.
You can capture the signals using a compatible Saleae analyser and use the MCP integration to inspect the captured data and decode the I2C communication.
Similarly, you can investigate UART communication, SPI transactions, clock signals and GPIO behaviour.
The project can also help with repeated signal captures and exporting analysis results for further investigation.
Practical applications:
- UART signal analysis.
- I2C communication debugging.
- SPI protocol analysis.
- Digital signal capture.
- Timing and pulse measurements.
- Hardware validation.
- Firmware regression testing.
This is particularly useful for engineers working on board bring-up, driver development and peripheral communication.
Note that this is a community-developed integration, not Saleae’s official MCP implementation.
Link: https://github.com/hsoffar/saleae-logic2-mcp
10. TinyMCP – Connect physical embedded devices with AI agents
The final project takes an interesting approach by connecting AI agents with physical embedded devices.
TinyMCP is an experimental project from Golioth that allows an MCP server running on a computer to communicate with remote embedded devices through the Golioth management platform.
It uses Golioth’s LightDB State and Remote Procedure Call capabilities to expose selected device functions as MCP tools.
For example, imagine that you have an ESP32-based IoT device connected to the Golioth platform.
The device contains an LED, temperature sensor and a few other peripherals.
You can expose selected device operations through an MCP tool schema. An AI assistant can then discover and invoke those operations through TinyMCP.
You could potentially ask an AI assistant to switch on an LED, retrieve a device reading or execute a supported remote procedure call.
One interesting aspect is that existing connected devices can expose supported operations without necessarily requiring changes to their firmware.
However, this depends on the device’s existing functionality and configuration.
The project uses the Golioth platform, so it is not a completely offline solution. You need a connected device, a Golioth project and the appropriate API credentials.
The project also explicitly warns that it is experimental and that delegating physical device control to AI requires caution.
Practical applications:
- Remote IoT device control.
- AI-assisted device monitoring.
- Sensor data retrieval.
- Remote procedure calls.
- Connected embedded systems.
- AI-powered IoT prototypes.
This is an interesting example of how MCP can connect AI applications with physical hardware beyond the development workstation.
Link: https://github.com/golioth/tinymcp
How can embedded engineers combine these MCP servers?
Individually, these projects solve different problems. But the real opportunity comes when you combine multiple MCP servers into a single development workflow.
Consider developing an ESP32-based IoT product.
A possible workflow could look like this:
Step 1: Firmware development
Use ESP-MCP or PlatformIO MCP to create your project, configure the microcontroller and build your firmware.
Step 2: Hardware communication
Use Embedded MCP or Serial MCP to communicate with your development board through UART and inspect the device logs.
Step 3: Debugging
Connect Embedded Debugger MCP or GDB MCP to inspect memory, registers, breakpoints and application execution.
Step 4: PCB development
Use KiCad MCP to inspect your schematic, trace component connections and validate your PCB design.
Step 5: Hardware testing
Use Saleae Logic 2 MCP to capture and analyse UART, SPI and I2C communication.
Step 6: Connected device management
Explore TinyMCP to expose selected device operations to AI agents through a connected IoT platform.
This creates an AI-assisted workflow that covers several stages of embedded product development.
What are the benefits of MCP for embedded engineers?
Here are some important areas where MCP integrations can help:
| Area | Potential benefit |
|---|---|
| Firmware development | Automate repetitive development commands |
| Debugging | Inspect errors and runtime information |
| Hardware communication | Interact with physical boards |
| PCB design | Analyse schematics and board connections |
| RTOS development | Access device and RTOS functionality |
| Testing | Automate selected validation workflows |
| Signal analysis | Decode and inspect communication |
| IoT | Expose remote device capabilities |
However, MCP does not replace an embedded engineer. AI assistants can misunderstand register configurations, generate incorrect firmware or misinterpret hardware signals. Always validate generated code, inspect commands and verify results on actual hardware.
Also, remember that not every project has the same maturity or licensing terms. Some are experimental, and some require proprietary development software or connected cloud services even when their MCP integration is open source. Check the repository’s current LICENSE file and documentation before adopting it.
Final thoughts
MCP is an interesting development for embedded engineers because it connects AI assistants with the tools that engineers already use.
Instead of limiting AI to generating C or C++ code, developers can start experimenting with AI-assisted firmware builds, debugging, serial communication, PCB inspection and hardware testing.
The most interesting possibility is creating a development environment where an AI assistant can help you write firmware, build it, flash it, inspect the UART output, analyse a failure and suggest improvements.
We are still at an early stage, and these tools are not replacements for proper engineering validation. But they provide a glimpse into how AI could become a useful part of the embedded development lifecycle.
