Hands-On: 하드웨어에게 두뇌를 주는 — AI Control Real Device를 하자 MCP Skills 구축
오븐을 말해주는 상상 "Preheat to 200 도 and bake for 25 분" — 그리고 그냥... 그것을. 앱 없음, 버튼 없음, 메뉴 없음. AI 에이전트에 의해 이해, 실제 하드웨어에서 실행.
이 가이드는exactly 어떻게 작동하나요?Smart 오븐 프로젝트는 TuyaOpen로 제작되었습니다. 끝에, 당신은 어떤 장치를 위한 당신의 자신의 기계설비 MCP 기술을 창조하는 방법을 알고 있을 것입니다.
What You'll Build
An AI-powered Smart Oven where a chat agent can:
| Command | What the Agent Does |
|---|---|
| "Turn on the oven" | Calls oven.start → sets the power DP |
| "Preheat to 200°C" | Calls oven.set_temperature(200) → writes the temp DP |
| "Bake for 25 minutes" | Calls oven.set_timer(1500) → sets countdown DP |
| "Is it still on?" | Calls oven.get_state() → reads all DPs, returns JSON |
| "Run the pizza recipe" | Calls oven.run_recipe("pizza") → sets temp + timer + starts |
| "Take a photo and check if the cake is done" | Calls device.camera_shot() → captures a JPEG the AI can see |
The magic glue? MCP Function Call — the protocol that turns hardware operations into tools an AI agent can discover and invoke.
어떻게 작동 : 건축
** 중요한 통찰력: ** 모든 하드웨어 기능(설정 온도, 읽기 상태, 캡처 이미지)는 MCP 도구로 등록되어 있습니다. AI 에이전트는 이러한 도구를보고, 사용자 의도를 기반으로 호출하는 결정, 그리고 콜백은 실제 하드웨어를 구동.
Step 1: Create the Cloud Product
Before writing any firmware code, you need a product on the Tuya cloud platform. The product defines your device's data model (DPs), AI agent, and cloud capabilities. Everything downstream — firmware, MCP tools, the app — depends on this.
1a. Create the product
- Log in to the Tuya Developer Platform → AI Product > Development → Create
- Select Custom to create a custom product from scratch (instead of picking a preset category)
- Complete the creation wizard — you'll get a PID (Product ID)
The fastest way: use the /tuya-iot-platform vibe coding skill in TuyaOpen IDE. Just describe your device in natural language and the Agent creates the product, defines DPs, and configures the AI Agent for you.
1b. Define Data Points (DPs)
Data Points (DPs) are the digital twin of your hardware. Each DP maps to a controllable or readable feature. In the Function Definition tab, click Add to create these custom DPs (IDs 101–199):
| DP ID | Code | Type | Range | Description |
|---|---|---|---|---|
| 101 | switch | Bool | — | Power on/off |
| 102 | temp_set | Value | 50–250 | Target temperature (°C) |
| 103 | temp_current | Value | 0–300 | Current temperature (°C) |
| 104 | timer | Value | 0–3600 | Countdown timer (seconds) |
Standard DPs (ID < 100) are pre-defined by Tuya. Custom DPs (ID 101–199) are yours to define. For the oven, all four are custom.
1c. Add AI Agent & MCP capabilities
In the Function Definition tab → Product AI Capabilities → Add Agent:
- Model Configuration → Skills Configuration → select Plugin → add Device Control · Bound Only (this enables MCP tool calls to your device DPs)
- Prompt Development → write a system prompt that describes the oven's capabilities so the AI knows when to call each tool
This creates the cloud-side Agent that will discover and invoke the MCP tools you register in firmware.
1d. Generate the DP header for firmware
Once DPs are defined on the cloud, generate the C header your firmware will include:
tuyaopen dp generate --target embedded
This produces tuya_dp_profile.h — the contract between cloud and device:
// tuya_dp_profile.h — auto-generated by `tuyaopen dp generate`
#define DPID_SWITCH 101
#define DPID_TEMP_SET 102
#define DPID_TEMP_CURRENT 103
#define DPID_TIMER 104
#define DPID_TEMP_SET_MIN 50
#define DPID_TEMP_SET_MAX 250
#define DPID_TIMER_MIN 0
#define DPID_TIMER_MAX 3600
The full product creation flow is documented in Create Your Product & Agent. For the oven demo, you can also let the AI Agent generate the product and DPs for you using the /tuya-iot-platform skill in TuyaOpen IDE.
단계 2: Mock 하드웨어 레이어 구현
MCP 도구를 배선하기 전에, 실제 하드웨어를 시뮬레이션하는 함수가 필요합니다. 이 도구 콜백을 깨끗하게 유지:
// app_oven.h
typedef struct {
bool switch_on;
int temp_set;
int temp_current;
int timer_remaining;
} oven_state_t;
OPERATE_RET app_oven_set_switch(bool on);
OPERATE_RET app_oven_set_temp(int temp_c);
OPERATE_RET app_oven_set_timer(int seconds);
OPERATE_RET app_oven_add_timer(int seconds);
oven_state_t app_oven_get_state(void);
// app_oven.c — each setter reports DPs to the cloud + updates the LCD
static oven_state_t g_oven_state = {
.switch_on = false,
.temp_set = 180,
.temp_current = 25,
.timer_remaining = 0
};
OPERATE_RET app_oven_set_temp(int temp_c) {
if (temp_c < DPID_TEMP_SET_MIN || temp_c > DPID_TEMP_SET_MAX)
return OPRT_INVALID_PARM;
g_oven_state.temp_set = temp_c;
__oven_report_dp(); // sync to cloud + LCD
return OPRT_OK;
}
Step 3: Register MCP Tools (The Core Step)
This is where the AI agent gets its "hands" on your hardware. Each tool is registered with:
- Name — what the AI sees (use dot-notation:
oven.set_temperature) - Description — LLM-friendly text explaining when/how to call it
- Parameters — typed input properties with ranges
- Callback — the function that runs when the AI calls this tool
The Registration Pattern
#include "ai_mcp_server.h"
#include "tal_event_info.h"
// Called once when MQTT connects (deferred registration)
static OPERATE_RET __oven_mcp_on_mqtt_connected(void *data) {
(void)data;
// Tool 1: Start the oven
TUYA_CALL_ERR_GOTO(AI_MCP_TOOL_ADD(
"oven.start",
"Turn on the oven. Use when the user wants to start cooking, "
"preheat, or begin baking.\nParameters: none\nReturns: bool",
__oven_start_cb, NULL
));
// Tool 2: Set temperature
TUYA_CALL_ERR_GOTO(AI_MCP_TOOL_ADD(
"oven.set_temperature",
"Set the oven target temperature in Celsius (50-250).\n"
"Parameters: temperature (int)\nReturns: int (applied temp)",
__oven_set_temp_cb, NULL,
MCP_PROP_INT_RANGE("temperature", "Target temperature in °C (50-250).",
DPID_TEMP_SET_MIN, DPID_TEMP_SET_MAX),
MCP_PROP_END
));
// Tool 3: Get full state
TUYA_CALL_ERR_GOTO(AI_MCP_TOOL_ADD(
"oven.get_state",
"Get the current oven state: power, target temp, current temp, "
"timer remaining.\nParameters: none\nReturns: JSON object",
__oven_get_state_cb, NULL
));
// ... more tools
return OPRT_OK;
}
OPERATE_RET app_oven_mcp_init(void) {
return tal_event_subscribe(
EVENT_MQTT_CONNECTED, "oven_mcp_tools",
__oven_mcp_on_mqtt_connected, SUBSCRIBE_TYPE_ONETIME);
}
The Callback Pattern
Each callback reads AI-supplied arguments from properties, calls the hardware function, and returns a result:
static OPERATE_RET __oven_set_temp_cb(const MCP_PROPERTY_LIST_T *properties,
MCP_RETURN_VALUE_T *ret_val,
void *user_data) {
// 1. Read the AI-supplied parameter
int temp = properties->properties[0]->value.int_val;
// 2. Call the hardware function
OPERATE_RET rt = app_oven_set_temp(temp);
// 3. Return the result to the AI
ai_mcp_return_value_set_int(ret_val,
(rt == OPRT_OK) ? temp : -1);
return OPRT_OK;
}
For JSON returns (like get_state):
static OPERATE_RET __oven_get_state_cb(const MCP_PROPERTY_LIST_T *properties,
MCP_RETURN_VALUE_T *ret_val,
void *user_data) {
oven_state_t s = app_oven_get_state();
cJSON *json = cJSON_CreateObject();
cJSON_AddBoolToObject(json, "switch_on", s.switch_on);
cJSON_AddNumberToObject(json, "temp_set", s.temp_set);
cJSON_AddNumberToObject(json, "temp_current", s.temp_current);
cJSON_AddNumberToObject(json, "timer_remaining", s.timer_remaining);
ai_mcp_return_value_set_json(ret_val, json);
return OPRT_OK;
}
단계 4: 철사 그것은 부트 Sequence에
내 계정app_chat_bot.c, MCP initright를 호출ai_mcp_init():
#if defined(ENABLE_COMP_AI_MCP) && (ENABLE_COMP_AI_MCP == 1)
TUYA_CALL_ERR_RETURN(ai_mcp_init());
TUYA_CALL_ERR_RETURN(app_oven_mcp_init()); // ← your tools
#endif
MQTT가 연결할 때 자동으로 도구 등록. 그게 다.
Step 5: Build and Test
cd source/embedded
tos.py build
Verify the tool appears in the agent's tool list, then try these interactions:
| You Say | Agent Calls | Result |
|---|---|---|
| "Preheat to 200 and bake for 25 minutes" | oven.set_temperature(200) → oven.set_timer(1500) → oven.start() | Oven heats up, timer counts down |
| "Roast a chicken" | oven.run_recipe("roast") | 200°C, 40 min, auto-start |
| "Is it still on? How hot?" | oven.get_state() | Returns {switch_on: true, temp_set: 200, ...} |
| "Take a photo and check if the cake is done" | device.camera_shot() | AI receives a JPEG and can visually inspect |
완전한 도구 세트
다음은 스마트 오븐을위한 MCP 도구의 전체 세트입니다 :
| 제품 정보 | 이름 * | 기타 제품 | 제품정보 |
|---|---|---|---|
oven.start | — | 한국어 | 힘에 |
oven.stop | — | 한국어 | 힘 떨어져 |
oven.set_temperature | temperature(int, 50-250) | 뚱 베어 | 설정 대상 온도 |
oven.set_timer | seconds(int, 0–3600) | 뚱 베어 | 설정 카운트 다운 |
oven.add_time | seconds(인치) | 뚱 베어 | 타이머에 시간을 추가 |
oven.get_state | — | 구글 맵 | 모든 DP를 읽으십시오 |
oven.list_recipes | — | JSON 배열 | 사전 설정 프로그램 |
oven.run_recipe | recipe(문자) | 구글 맵 | 요리법 + 시작 |
device.camera_shot | — | 이미지/jpeg | 사진 캡처 |
내장 요리법
| 뚱 베어 | 사이트맵 | (주) | 제품 정보 |
|---|---|---|---|
bake | 180°C에 | 30분 | 케이크, 빵, 카사 |
roast | 200°C의 | 40분 | 고기와 야채 |
broil | 230°C의 | 10분 | 빠른 갈색 |
pizza | 220°C의 | 15분 | 높은 열 피자 |
grill | 250°C의 | 8분 | 숙박 약관 |
reheat | 120°C에 | 5 분 | 좌로 |
warm | 80°C에 | 30분 | 따뜻한 유지 |
AI Coding Prompts: Tips for Developers
When using AI coding assistants (Cursor, Claude Code, Copilot) to build hardware MCP skills, these prompt patterns will accelerate your development:
Prompt Pattern 0: Generate the Full Cloud Product from a Description
/tuya-iot-platform
Create a new AI product for a smart oven with these capabilities:
- Power on/off (bool)
- Temperature setting 50-250°C (value)
- Current temperature readback 0-300°C (value, read-only)
- Countdown timer 0-3600 seconds (value)
Add an AI Agent with device control MCP plugin.
Generate the DP definitions and the embedded DP header.
Why it works: The /tuya-iot-platform skill creates the cloud product, defines DPs, configures the AI Agent, and generates the firmware DP header — all from a natural-language device description. This is the fastest way to go from idea to code.
Prompt Pattern 1: Describe the Device, Not the Code
I have a smart oven with these features:
- Power on/off
- Temperature control (50-250°C)
- Timer (0-3600 seconds)
- Current temperature sensor
- Camera for visual inspection
Create MCP tool registrations for each feature.
Use the AI_MCP_TOOL_ADD macro pattern from the otto_robot example.
Why it works: The AI maps your device description directly to tool names, descriptions, and parameters.
Prompt Pattern 2: Specify the DP Mapping
My oven DPs are:
- DP 101: switch (bool, rw)
- DP 102: temp_set (value, 50-250, rw)
- DP 103: temp_current (value, 0-300, ro)
- DP 104: timer (value, 0-3600, rw)
Generate the tuya_dp_profile.h header and the MCP tool callbacks
that read/write these DPs.
Why it works: Explicit DP definitions eliminate ambiguity about parameter types and ranges.
Prompt Pattern 3: Request LLM-Friendly Descriptions
For each MCP tool, write descriptions that help an LLM understand:
1. WHEN to use this tool (what user intent triggers it)
2. WHAT parameters it takes (with units and ranges)
3. WHAT it returns (type and meaning)
Example: "Set the oven target temperature in Celsius (50-250).
Use when the user says 'preheat', 'set temp', or 'bake at X degrees'."
Why it works: Good tool descriptions are the #1 factor in the AI picking the right tool.
Prompt Pattern 4: Ask for Error Handling
Add input validation to each MCP tool callback:
- Clamp temperature to the DP range (50-250)
- Return -1 on invalid parameters
- For run_recipe, return {ok: false, available: [...]} on unknown recipe names
Why it works: The AI can self-correct when it gets structured error responses.
Prompt Pattern 5: Generate the Full Stack at Once
/tuyaopen-dev-loop
Create a complete Smart Oven project:
1. Cloud product with DPs (switch, temp_set, temp_current, timer)
2. Embedded firmware with mock hardware (app_oven.c)
3. MCP tools for all oven features (app_oven_mcp.c)
4. LVGL UI showing oven state on the T5-AI board display
5. Wire everything in app_chat_bot.c
Why it works: The /tuyaopen-dev-loop skill orchestrates the full cloud-to-device workflow.
키 테이크아웃
- **클라우드 제품 시작 ** — 제품을 만들고 DP를 정의하고, 펌웨어를 터치하기 전에 클라우드 플랫폼에 AI 에이전트를 추가합니다. 제품은 기초입니다.
- **DP는 계약 ** - 클라우드에 정의, 헤더를 생성, 다른 모든은 다음과 같습니다.
- Tool descriptions issues — LLMs에 대한 쓰기: 사용할 때, 어떤 params, 어떤 반환.
- ** MQTT에 등록은 ** — 사용
tal_event_subscribe(EVENT_MQTT_CONNECTED, ..., SUBSCRIBE_TYPE_ONETIME)그래서 클라우드 링크가 준비 될 때 도구 등록. - ** MCP 별도의 Keep 하드웨어 ** — Your
app_oven.c손잡이 기계설비;app_oven_mcp.c핸들 도구 등록. 깨끗한 분리. - Return Structured data - JSON 응답
ok: false+ 유효한 선택권은 AI self-correct를 시켰습니다.
Next Steps
- Create Your Product & Agent — Full guide to creating the cloud product, DPs, and AI Agent
- Custom Device MCP (Hardware Skills Guide) — Full API reference for MCP tool development
- Hardware Peripheral Skills — Built-in GPIO, ADC, I2C, UART, PWM tools
- MCP Server API — Complete MCP server documentation
- Designing Device MCP Tools — Best practices for tool design