Simple chat with MCP support - Electron app
Simple chat with MCP support - Electron app. Exposed over MCP by the simplechatjs mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
An Electron desktop AI chat application with MCP (Model Context Protocol) support.
Everything the assistant can do here goes through one of these:
Ollama — Local AI modelsAnthropic — Claude (via proxy or direct)Prerequisites — The Prerequisites tool exposed by this serverInstallation — The Installation tool exposed by this serverBuild — The Build tool exposed by this servernpm on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
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. SimpleChatJS's toolset — Ollama, Anthropic, Prerequisites and 2 more — is a fair guide to whether it matches your workflow. It is maintained by Tomobobo710; 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 |
|---|---|
| Ollama | Local AI models |
| Anthropic | Claude (via proxy or direct) |
| Prerequisites | The Prerequisites tool exposed by this server. |
| Installation | The Installation tool exposed by this server. |
| Build | The Build tool exposed by this server. |
{
"mcpServers": {
"simplechatjs": {
"command": "npx",
"args": ["-y", "npm"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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.