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by voska · MCP Server · ★ 341
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Hass-MCP A Model Context Protocol (MCP) server for Home Assistant integration with Claude and other LLMs. Overview Hass-MCP enables AI assistants like Claude to interact directly with your Home Assistant instance, allowing them to: Query the state of devices and sensors Control lights, switches, and other entities Get summaries of your smart home Troubleshoot automations and entities Search for specific entities Create guided conversations for common tasks Screenshots Features Entity Management: Get states, control devices, and search for entities Domain Summaries: Get high-level information about entity types Automation Support: List and control automations Guided Conversations: Use prompts for common tasks like creating automations Smart Search: Find entities by name, type, or state Token Efficiency: L
| Stars | 341 |
| Forks | 52 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 75.4219253068713/100 |
| Open Issues | 8 |
| Last Updated | 2026-08-06 |
| Created | 2025-03-16 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~16k |
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hass-mcp is Control and query Home Assistant from Claude and other LLMs — a Model Context Protocol (MCP) server.. It is categorized as a MCP Server with 341 GitHub stars.
hass-mcp is primarily written in Python. It covers topics such as ai, anthropic, claude.
You can find installation instructions and usage details in the hass-mcp GitHub repository at github.com/voska/hass-mcp. The project has 341 stars and 52 forks, indicating an active community.
hass-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to hass-mcp on Agent Skills Hub include maverick-mcp, claude-historian-mcp, youtube-connector-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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