Making Agent Tools Feel Instant: iMessage Gateway CLI

The Observation

Agent workflows fail in a boring way: latency.

If a tool takes long enough to feel “noticeable,” the workflow collapses into hesitation, retries, and context switching. For local tools, MCP startup overhead can dominate the actual operation time.

Here’s the mental model I used while building the gateway:

MCP vs CLI: Where the Time Goes

In many local integrations, the work is fast (querying a database, serializing results), but the protocol + process startup cost is slow.

The goal of the iMessage Gateway CLI is simple:

Keep common operations in the “instant” range for high-frequency agent usage.

Architecture

The gateway is a macOS-first tool that talks directly to the local Messages database and surfaces a stable CLI interface agents can call repeatedly.

Agent / Script
  ├─ wolfies-imessage (CLI)
  └─ optional daemon (warm path)
        └─ Messages SQLite DB (local)

The crucial tradeoff is intentional:

• MCP: best for rich tool discovery + deep integration
• CLI/daemon: best for fast repetitive calls

Benchmarks (Why This Exists)

The gateway includes a benchmark suite to keep performance honest.

The results aren’t just “a bit faster.” They move the tool into a different user perception tier:

• Startup becomes small enough to be ignored
• Most ops are consistently sub-100ms

Benchmark Snapshot

The numbers below are a pragmatic comparison against MCP-style flows where startup overhead is paid repeatedly.

The takeaway isn’t “my code is faster.” It’s that eliminating startup overhead changes the user perception tier.

Making It Safe

Reading Messages requires macOS permissions and careful handling of personal data.

Key choices:

• Local-first: data stays on the machine
• Minimal outputs: return only what’s requested
• Benchmarkable behaviors: performance regressions are visible

Takeaways

1. The user experience is dominated by startup costs more often than the “real” work.
2. A “protocol-correct” approach can still be UX-wrong for high-frequency agent calls.
3. If a tool’s median behavior is slow, agents become unreliable—even if the tool is correct.