Wall-clock awareness for LLM agents. Two tools: elapsed-time-between-turns + day rollover detection.
If you already use Temporal, the temporal mcp server is the piece that lets your assistant work with it directly. Wall-clock awareness for LLM agents. Two tools: elapsed-time-between-turns + day rollover detection.
temporal-mcp is a tiny Model Context Protocol server that gives LLM agents a sense of time between turns. Two tools, a few hundred lines, stdlib + mcp + platformdirs. That's the whole thing.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
temporal_tick — Advance the clock for a thread and return a snapshot. Call once per user turn.temporal_peek — Read-only. Same shape, doesn't advance state. Use it when you want the gap delta but the call isn't the canonical "one tick per user turn" eventBecause this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
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. Temporal's toolset — temporal_tick, temporal_peek — is a fair guide to whether it matches your workflow. It is maintained by MirrorEthic; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Temporal's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| temporal_tick | Advance the clock for a thread and return a snapshot. **Call once per user turn.** |
| temporal_peek | Read-only. Same shape, doesn't advance state. Use it when you want the gap delta but the call isn't the canonical "one tick per user turn" event. |
#### Claude Desktop
```json
{
"mcpServers": {
"temporal": {
"command": "temporal-mcp"
}
}
}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.