MCP-Think is a Model Context Protocol (MCP) server that implements a "[Think Tool](https://www.anthropic.com/engineering/claude-think-tool)" for
If you already use MCP Think, the mcp think mcp server is the piece that lets your assistant work with it directly. MCP-Think is a Model Context Protocol (MCP) server that implements a "Think Tool" for LLMs. This tool allows LLMs to record and retrieve their thinking processes during reasoning.
The server ships on npm as @smithery/cli, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Requirements — The Requirements tool exposed by this servergo install/go run)This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. MCP Think's toolset — Requirements — is a fair guide to whether it matches your workflow. It is maintained by iamwavecut; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against MCP Think's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Requirements | The Requirements tool exposed by this server. |
{
"mcpServers": {
"think": {
"command": "npx",
"args": ["-y", "@smithery/cli"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
go install/go run)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.