An MCP server that gives LLMs temporal awareness and time calculation
This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location /…
In plain words
This open-source Model Context Protocol server adds temporal awareness to large language models through six functions including current datetime, time difference calculations, and timestamp context. It enables Claude and GPT to understand time passage, detect pauses, and reason about patterns in conversations. The tool runs locally in Python in under 60 seconds or via a hosted demo, giving LLMs a new sensory capability beyond text prompts to recognize rhythms and structural patterns. It's intended for developers building AI applications that need contextual time understanding.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location / movement (GPS, speed, “I’m on a train”) - Weather (rainy evening vs clear morning) - Device state (battery low, poor bandwidth) - Ambient modality (user is dictating on mobile vs typing at desk) - Calendar context (meeting starts in 5 min) - Biometric cues (heart‑rate spikes while coding) Curious what other signals people think would unlock better collaboration. Full back story: https://medium.com/@jeremie.lumbroso/teaching-ai-the-signifi... Happy to discuss MCP patterns, tool discovery, or future “senses”. Feedback and PRs welcome!
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