# Tigo Energy MCP Server

> Use this tool when you need to integrate AI-powered monitoring and analytics into your solar energy system, providing access to production data, performance metrics, and system health information. It solves problems related to solar system optimization, maintenance, and performance tracking by enabling AI assistants to retrieve and analyze data from Tigo Energy solar systems. The tool accepts API requests and returns comprehensive data and analytics outputs, ideal for use cases requiring real-time solar system monitoring and insights.

Canonical page: https://skillsregistry.net/skills/matt-dreyer-tigo-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/matt-dreyer-tigo-mcp-server

## Description

A Model Context Protocol (MCP) server that provides comprehensive access to Tigo Energy solar system data and analytics. It enables AI assistants to interact with your Tigo solar monitoring system to retrieve production data, performance metrics, system health information, and maintenance insights.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** data-analytics
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/oo0cy4j2wg)
- **Repository:** <https://github.com/matt-dreyer/Tigo_MCP_server>

## 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": "matt-dreyer-tigo-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/matt-dreyer-tigo-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/matt-dreyer-tigo-mcp-server/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