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Connect cloud LLM agents to edge and IoT hardware via standard MCP tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP-Edge" yet — see the docs or source repo.
Use MCP-Edge to discover available UART devices, connect to the temperature and humidity sensor, read the latest 10 samples, and return structured JSON.
A list of discovered UART devices, a successful connection to the target sensor, and JSON output containing 10 temperature and humidity readings.
Use MCP-Edge to scan nearby BLE devices, find the device named smart-lock-01, read its battery status, and execute a lock command.
Returns the matched BLE device details, battery reading, and the execution status of the lock command.
Use MCP-Edge to discover accessible IoT devices on the local network, filter online cameras, check firmware versions, and mark devices that need upgrades.
Outputs a list of online cameras, their firmware versions, and a list of devices requiring upgrades.
Enable LLMs to control embedded devices through the Model Context Protocol.
Build MCP servers with embedded reasoning for efficient complex task handling.
Turn existing APIs and databases into MCP tools for direct AI use.
Offload non-critical LLM tasks to your own model and save premium quota.
Delegate low-risk tasks to a cheaper model with main-agent review.
Offload non-critical LLM tasks to your own model to save premium quota.