Context7
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Description from the maker (original English)
Wall-clock awareness for LLM agents. Two tools: elapsed-time-between-turns + day rollover detection.
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This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"temporal": {
"command": "uvx",
"args": [
"temporal-mcp"
]
}
}
}Run this in your terminal
claude mcp add --transport http temporal https://temporal-mcp.dev/mcp --header 'Authorization: your-value'claude mcp add temporal -- uvx temporal-mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"temporal": {
"url": "https://temporal-mcp.dev/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"temporal": {
"command": "uvx",
"args": [
"temporal-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"temporal": {
"type": "http",
"url": "https://temporal-mcp.dev/mcp",
"headers": {
"Authorization": "your-value"
}
}
}
}Paste into .vscode/mcp.json in your project
{
"servers": {
"temporal": {
"type": "stdio",
"command": "uvx",
"args": [
"temporal-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.
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We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account MirrorEthic, which owns the repo
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Your model knows calculus but not what day it is. Fix that.
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.
Open a fresh chat at 11 PM. The model says "good morning." Resume a conversation three weeks later. The model picks up mid-sentence like no time passed. Ask for "today's status." Get yesterday's status. Or last Tuesday's.
LLMs don't have wall clocks. They don't know when the last user message was, whether the calendar flipped, or whether this is a fresh thread or one resumed after a long gap. Most of the time this is harmless. Sometimes it makes your agent sound like it just woke up from cryosleep.
A persistent per-thread last-seen log, exposed as two MCP tools:
That's it. Time exists. Your model should know that.
curl -s -X POST https://temporal-mcp.dev/mcp \
-H "Content-Type: application/json" \ -H "Authorization: Bearer $(uuidgen)" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{ "name":"temporal_tick", "arguments":{"thread_key":"try-it","tz_offset_minutes":-360,"tz_name":"MDT"}}}' \ | python3 -c 'import sys,json; print(json.load(sys.stdin)"result"0)'
You'll see something like:
[temporal] Wed May 13, 10:42 AM MDT | fresh thread (no prior history)
{...JSON payload...}
Run it twice and the second response shows the gap. Run it tomorrow and you'll get day rollover: yes. That's the whole point.
Every tick returns a human-readable header and a JSON payload:
[temporal] Wed May 13, 9:42 AM MDT | last prompt 14m ago (Wed 9:28 AM)
{"thread_key": "mcp:abc123", "now": 1747158120.0, "prev": 1747157280.0, "delta_sec": 840, "day_rollover": false, "fresh_thread": false, "tz_name": "MDT", "tz_offset_sec": -21600, "available": true, "error": ""}
The header is for the model. The JSON is for your code, in case you want to do something interesting with day_rollover (greet differently, reload context, recompute "today's items") or with delta_sec (decay relevance, detect a resumed session, flag idle threads).
If you use claude.ai web, ChatGPT, or anything else that wants a *remote* MCP server, point your connector at:
https://temporal-mcp.dev/mcp
There are two ways to authenticate, depending on what your client UI exposes:
Both claude.ai and ChatGPT's custom connector UIs require OAuth 2.0 with a Client ID and Client Secret. The hosted endpoint is a full OAuth provider — visit https://temporal-mcp.dev/connect and click Generate OAuth Credentials. You'll get a fresh client_id + client_secret pair, shown once. Paste them into your client's connector config. That's the entire signup.
No email, no password, no account record — the credential pair *is* the identity. We store only a SHA-256 of the secret, so we never see the plaintext. Generate a new pair any time you want a fresh timeline.
Claude.ai setup: Settings → Connectors → Add custom connector. URL https://temporal-mcp.dev/mcp. Paste your Client ID and Client Secret. Connect. The auto-approve flow redirects you back, claude.ai exchanges the code for a token, and you're done.
ChatGPT setup: Same idea — Settings → Connectors → Custom MCP. Same URL, same credentials.
If your client supports custom HTTP headers (most do), skip OAuth and just send any opaque string as a bearer token:
Authorization: Bearer <any opaque string you choose>
Pick a UUID, a passphrase, anything. We SHA-256 it before storing anything; same identity-is-the-credential property as the OAuth flow, without the dance. This is the original lowest-ceremony path and works for any client that lets you set a custom header.
If your client's connector UI only exposes a URL field — no headers, no auth, no OAuth — embed your token directly in the path:
https://temporal-mcp.dev/mcp/<any opaque string you choose>
Or as a query parameter, if the path form gets stripped:
https://temporal-mcp.dev/mcp?token=<any opaque string you choose>
Same SHA-256 hashing, same identity model. URL-embedded tokens leak more easily than header tokens (proxy logs, referrers), so this path is a pragmatic fallback rather than the default — but the threat in our model is "someone advances your timeline," not data exposure. Rotate by picking a new random string any time you suspect the URL has been logged where it shouldn't be.
No signup. No email. No PII. The hosted endpoint is free, rate-limited to 60 requests/minute per credential. If you outgrow that, self-host (see below).
For desktop/IDE MCP clients, pip install the Python package and run it locally. No network round-trip, state lives on your disk, no auth needed.
pip install temporal-mcp
Python 3.9+. Linux, macOS, Windows.
Run as stdio:
temporal-mcp # or: python -m temporal_mcp
{
"mcpServers": { "temporal": { "command": "temporal-mcp" } } }
Same idea — point the client at the temporal-mcp command.
The hosted endpoint at temporal-mcp.dev runs on Cloudflare Workers backed by D1. If you want your own instance — for privacy, scale, or to ship it as part of a larger product — the entire deploy lives in workers/:
cd workers
npm install npx wrangler login npx wrangler d1 create temporal_mcp # creates the database
npx wrangler d1 migrations apply temporal_mcp --remote npx wrangler deploy
Free tier covers ~100k requests/day forever. Set REQUIRE_AUTH=true in [vars] to refuse anonymous traffic. The Worker is ~400 lines of TypeScript and has its own unit tests (workers/test/).
temporal_tickAdvance the clock for a thread and return a snapshot. Call once per user turn.
| Field | Type | Notes |
|---|---|---|
thread_key | string, optional | Stable conversation/session ID. claude.ai web: conversation ID. Cursor: window/workspace ID. Anything else: any caller-stable string. Omit it and you get a default hostname+cwd hash — fine for local testing, not for serving multiple threads. |
client_id | string, optional | Namespace tag (e.g. "caweb", "cursor"). Defaults to "mcp". Use distinct tags per client so threads don't collide in shared state. |
temporal_peekRead-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.
Per-thread last-seen state lives at:
| Platform | Path |
|---|---|
| Linux | ~/.local/share/temporal-mcp/state.json |
| macOS | ~/Library/Application Support/temporal-mcp/state.json |
| Windows | %LOCALAPPDATA%\temporal-mcp\state.json |
Override with TEMPORAL_MCP_STATE_DIR=/some/path.
State writes are flock-safe on POSIX and atomically replaced via os.replace, so multiple agents pointing at the same state directory will not corrupt each other. (Windows falls back to an in-process lock — fine for a single MCP server, not designed for cross-process contention.)
python -m temporal_mcp gc # prune threads > 30d idle
python -m temporal_mcp gc 7 # prune threads > 7d idle
Not exposed as an MCP tool on purpose — a model that can prune its own memory of "when did we last talk" will eventually do it at exactly the wrong moment. Run it from cron if you care.
{client_id}:{key}. Reserve a unique client_id per surface so threads from claude.ai web don't collide with a local Cursor session sharing the same state directory.available: false with an error field and the header says gap: unknown. It does not silently lie and call it a fresh thread — a model that thinks every turn is fresh will keep saying good morning forever.tick() runs in a daemon thread with a 100 ms timeout so a stalled state read can't block your hook budget. If it times out, you get the honest-failure snapshot above.Mcp-Session-Id and friends, configurable timezone overrideresume: true flag past N hours) so agents can branch on resumed sessions without doing the math themselvesMIT. See LICENSE.
Built by Garret Sutherland / MirrorEthic LLC, extracted from the temporal layer of a larger cognitive-mesh project where this primitive was load-bearing enough to deserve its own package.
mcp-name: io.github.MirrorEthic/temporal-mcp