lumbroso

It's good idea. I didn't think of it because this project came about a "let's try to write a remote MCP server now that the standard has stabilized."

But there are some issues:

1. Cheaper + Deterministic: It is much more costly, both in terms of tokens and context window. (Generating the code takes many more tokens than making a tool call.) And there can be variability in the query, like issues with timezones.

2. Portability: It is not portable, not all LLM or LM environments have access to a code interpreter. This is a much lower resource requirement.

3. Extensibility: This approach is extensible, and it allows us to expand the toolkit with additional cognitive scaffolds that help contextualize how we experience time for the model. (This is a fancy way of saying: The code only gives the timestamp, but building an MCP allows us to contextualize this information — "this is time I'm sleeping, this is the time I'm eating or commuting, etc.")

4. Security: Ops teams are happier approving a read-only REST call than arbitrary code running.

Glad the little political wink landed with at least one reader!

You’re right: Stripping away all ambient context is both a bug and a feature. It lets us rebuild “senses” one at a time—clean interfaces instead of the tangled wiring in our own heads.

Pauses are the first step, but I’m eager to experiment with other low‑bandwidth signals:

• where the user is (desk vs. train) • weather/mood cues (“rainy Sunday coding”) • typing vs. speech (and maybe sentiment from voice) • upcoming calendar deadlines

If you could give an LLM just one extra sense, what would you pick—and why?

Noted, thank you!

Great question! Injecting a raw epoch each turn can work for tiny chats, but a tool call solves four practical problems:

1. *Hands‑free integration*: ChatGPT, Claude, etc. don’t let you auto‑append text, so you have to manually do it. Here, a server call happens behind the scenes—no copy‑paste or browser hacks.

2. *Math & reliability*: LLMs core models are provably not able to do math (without external tools), this is a theoretical limitation that will not change. The server not only returns now() but also time_difference(), time_since(), etc., so the model gets ready‑made numbers instead of trying to subtract 1710692400‑1710688800 itself.

3. *Extensibility*: Time is just one "sense." The same MCP pattern can stream location, weather, typing‑vs‑dictation mode, even heart‑rate. Each stays a compact function call instead of raw blobs stuffed into the prompt.

So the tool isn’t about fancy code—it’s about giving the model a live, scalable, low‑friction sensor instead of a manual sticky note.

One‑shot timestamps (the kind hard‑coded into Claude’s system prompt or passed once at chat‑start) go stale fast. In a project I did with GPT‑4 and Claude during a two‑week programming contest, our chat gaps ranged from 10 seconds to 3 days. As the deadline loomed I needed the model to shift from “perfect” suggestions to “good‑enough, ship it” advice, but it had no idea how much real time had passed.

With an MCP server the model can call now(), diff it against earlier turns, and notice: "you were away 3 h, shall I recap?" or "deadline is 18 h out, let’s prioritise". That continuous sense of elapsed time simply isn’t possible with a static timestamp stuffed into the initial prompt; you'd have to create a new chat to update the time, and every fresh query would require re‑injecting the entire conversation history. MCP gives the model a live clock instead of a snapshot.

Hey, thanks so much for sharing, your repo is really cool, including the GEMINI.md context engineering file!

I am curious: You say "offline-first or local-first, quantified self projects", what models do you use with your projects?

I find the LLMs like the Claude and GPT families to be incredibly impressive for integration and metacognition — however, I am not sure yet what LMs are best for that purpose, if there are any.

Your "Augmented Awareness" framework seems to be metacognition-on-demand. In practice, how has it helped you recently? Is it mostly automated, or does it require a lot of manual data transfers?

I am assuming that the MCP server is plugged into a model, and that in the model you run prompts to run retrospectives.

Have you written about this?

I'm sorry for choosing an inappropriate title — that was my bad, and fortunately @dang helped correct this mistake.

Aside from the title, what claims do I make that you find ridiculous?

Thanks so much for the title change! I completely understand.

I apologize to the community for the mistake. I appreciate this feature of this community's discourse. I'll remember to use literal, precise language in the future.

Your reworded title fits perfectly — thank you!

Disposal8433, I am not unsympathetic to your point, but I think that bad managers and CEOs are bad managers and CEOs.

For instance at Boeing, the fault of software problems lies entirely on the managers: They made the decision to subcontract software engineering to a third party to cut cost, but also they didn't provide the contractor with enough context and support to do a good job. It's not subcontracting that was bad — because subcontracting can be the solution in some circumstances and with proper scoping and oversight — it was the management.

The MCP protocol is changing every few weeks, it doesn't make sense (to me at least) to professionalize a technical demo, and I appreciate that LLMs allow for faster iteration and exploration.

I'm sorry for the misleading title about a sundial, it was a metaphor, and based on the feedback here, if I had to do it again I would pick a different one. :-)

My only worry with these MCP "sensors" is that they add-up to the token cost — and more importantly to the context window cost. It would be great to have the models regularly poll as new data and factor that into their inferences. But I think the models (at least with current attention) will always have a trade-off between how much they are provided and what they can focus on. I am afraid that if I provide Claude numerous senses, that it will lower its attention to our conversation.

But your exciting comment (and again I apologize for disappointing you!) makes me think about creating an MCP server that provides like the position of the sun in the sky for the current location, or maybe some vectorized representation of a specific sundial.

I think the digitized information that we experience is more native to models (i.e., require fewer processing steps to extract insights from), but it's possible that providing them this kind of input would result in unexpected insights. They may notice patterns, i.e., more grumpy when the sun is in this phase, etc.

Thanks for your thoughtfulness!

That's exactly what I've been thinking too!

MCP + LLMs = our solution to data integration problems, which include context awareness limitations.

It's an exciting development and I am glad you see it too!

Fair point on the metaphor—let me be concrete.

What’s new here isn’t just exposing `current_datetime()`. The server also gives the model tools to reason about time:

  (1) time_difference(t1, t2)  – exact gaps with human wording  

  (2) timestamp_context(t)      – “weekend evening”, “workday morning”  

  (3) time_since(t)             – “2 h ago, earlier today”  

I also request that Claude ask for time at every turn, which creates a timeseries that is parallel to our interactions. When Claude calls these every turn it starts noticing patterns (it independently labelled our chat as a three-act structure). That was the surprise that prompted the title.

Ask Claude “what patterns do you see so far?” after a few exchanges.

If you still find it trivial after trying, happy to hear why—genuinely looking for ways to push this further. Thanks for the candid feedback.

Finding a good title is really hard. I'd appreciate any advice on that. You'll notice I wrote the article several weeks ago, and that's how long it took me to figure out how to pitch on HN. I'd appreciate any feedback to improve. Thanks!