MCP server that sends native notifications with LLM-aware icons
MCP server that sends native notifications with LLM-aware icons. Exposed over MCP by the mcpal mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Lightweight MCP server for native desktop notifications with action buttons, text replies, and LLM-aware icons.
mcpal on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Everything the assistant can do here goes through one of these:
Parameters — The Parameters tool exposed by this serverExamples — You can reply directly from the notification without switching apps:Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Mcpal's toolset — Parameters, Examples — is a fair guide to whether it matches your workflow. It is maintained by mjkid221; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Parameters | The Parameters tool exposed by this server. |
| Examples | You can reply directly from the notification without switching apps: |
// JSON
{
"mcpServers": {
"mcpal": {
"command": "npx",
"args": ["mcpal@latest"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.