There's a whole category of LLM failure modes that only becomes visible when you give the model a tool and then watch what it chooses not to do. This project — an MCP server that simply exposes the current time — is a beautifully minimal probe of exactly that behavior. The premise: hand the model a clock, ask it time-sensitive questions, and observe how often it just... makes up an answer instead of calling the tool that's right there.

The finding hinted at in the title ("usually won't look at it") matches what a lot of practitioners have been quietly noticing but rarely benchmark rigorously. Models are trained to answer, not to check. Even when tool use is available, cheap, and clearly relevant, they default to their prior — which for temporal questions is a static training-cutoff hallucination dressed up as confidence. A model will happily tell you "it's currently around 3pm on a Tuesday" without ever invoking the tool sitting one function call away.

Why this matters for anyone building agentic systems:

• Tool availability ≠ tool usage. Adding an MCP server to your stack doesn't guarantee the model reaches for it. Prompting, tool descriptions, and system instructions all shape invocation rates in ways that are still poorly understood.
• Clocks are a canary. Time is the simplest possible external state — one integer, no ambiguity, no reasoning required. If a model won't check a clock, it probably won't check your database, your feature flags, or your rate limiter either when it thinks it can guess.
• It's a testable, reproducible artifact. Unlike vague complaints about hallucination, "did the model call tick_now() or not?" is a binary you can measure across models, prompts, and MCP client implementations.

The MCP angle is what elevates this from a cute demo to something useful. MCP is the emerging standard for wiring tools into LLM clients, and tick-mcp doubles as a diagnostic — drop it into Claude Desktop, Cursor, or any MCP-compatible harness and you get a cheap, honest read on whether the model is actually reaching for context or just confabulating around it.

For a Show HN with one point and zero comments, it's punching well above its weight as a research artifact. The kind of thing that deserves a blog post, a leaderboard, and probably a paper.