mcp-assist MCP Server
mike-nott/mcp-assist
[](https://agentrank-ai.com/tool/mike-nott--mcp-assist/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) MCP-powered Home Assistant conversation agent that solves entity context limitations through dynamic discovery instead of full entity dumps
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
mike-nott/mcp-assist is a Python tool licensed under MIT. MCP-powered Home Assistant conversation agent that solves entity context limitations through dynamic discovery instead of full entity dumps Topics: conversation-agent, hacs, home-assistant, home-assistant-custom-component, mcp.
Ranked #15860 out of 24846 indexed tools.
Ecosystem
Score Breakdown
110 stars → growing community
Last commit 3mo ago → stale
29/54 issues closed → decent issue management
3 contributors → small team
No dependents → no downstream usage
582 weekly installs → early adoption
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# MCP Assist for Home Assistant A Home Assistant conversation agent that uses MCP (Model Context Protocol) for efficient entity discovery, achieving **95% token reduction** compared to traditional methods. Works with LM Studio, llama.cpp, Ollama, OpenAI, Google Gemini, Anthropic Claude, and OpenRouter. ## Key Features - ✅ **95% Token Reduction**: Uses MCP tools for dynamic entity discovery instead of sending all entities - ✅ **No Entity Dumps**: Never sends 12,000+ token entity lists to the LLM - ✅ **Smart Entity Index**: Pre-generated system structure index (~400-800 tokens) for context-aware queries - ✅ **Multi-Platform Support**: Works with LM Studio, llama.cpp, Ollama, OpenAI, Google Gemini, Anthropic Claude, and OpenRouter - ✅ **Multilingual Support**: 21 languages with localized UI, system prompts, and speech detection - ✅ **Multi-turn Conversations**: Maintains conversation context and history - ✅ **Dynamic Discovery**: Finds entities by area, type, device_class, state, or naRead full README on GitHub →
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