# MCP-Edge

> Use this tool when you need to integrate cloud-based Large Language Models (LLMs) with physical hardware on edge and IoT devices, solving connectivity issues and enabling seamless interaction with constrained devices. It bridges device channels like UART, BLE, and Wi-Fi, allowing for standard MCP tool invocation. This enables efficient communication and control of edge devices, making it ideal for use cases requiring real-time data exchange and device management.

Canonical page: https://skillsregistry.net/skills/jemsbhai-mcp-edge  
JSON: https://api.skillsregistry.net/v1/skills/jemsbhai-mcp-edge

## Description

Enables cloud LLM agents to discover and invoke physical hardware on edge and IoT devices through standard MCP tools, bridging constrained device channels like UART, BLE, and Wi-Fi.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** iot-hardware
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/n4dgwzb8la)
- **Repository:** <https://github.com/jemsbhai/mcp-edge>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "jemsbhai-mcp-edge"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jemsbhai-mcp-edge` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jemsbhai-mcp-edge/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt