Debugging iOS Memory Issues from Your AI Chat Interface
The Context-Switching Tax
Here's a scenario I lived through more than once: an app crash comes in overnight. I open Xcode Organizer to pull the crash report. Then I open Terminal to run log show and see what the system was doing around that time. Then I open the leaks tool to check for retained objects. Then I go back to Claude to explain everything I found and ask for help interpreting the backtrace.
Four different interfaces. None of them talking to each other. And Claude — the tool actually helping me debug — is sitting in a separate window, completely blind to what's on my screen.
The obvious fix: what if Claude could be the diagnostic interface?
What It Is
I built a Python MCP server (using FastMCP) that exposes eight tools for iOS runtime diagnostics directly to Claude. Once registered, your booted simulators and connected physical devices become part of the conversation. You can ask Claude to list running apps, pull recent crashes, check memory risk, stream live logs, fire memory warnings, and run leak detection — all without leaving the chat window. It works with both Claude Code and Claude Desktop.
The Jetsam Surprise
Most iOS developers know that iOS will kill your app if it uses too much memory. Fewer know the exact mechanism: Jetsam, Apple's kernel-level memory manager, maintains a per-device memory budget for each app. When your app's RSS (resident set size) crosses that budget, Jetsam terminates it — instantly, silently, with no chance to save state.
The catch is that the Jetsam limit isn't uniform. It varies dramatically by device. An iPhone 16 Pro (8 GB RAM) allows roughly 4,000 MB per app. An iPhone SE 2nd/3rd gen (3 GB RAM) allows roughly 900 MB. The same app using 780 MB of memory is perfectly healthy on a Pro device and critically close to death on an SE.
This matters for a specific, painful reason: most developers build and test on high-end hardware or simulators that mirror high-end hardware. The users getting killed are often on the older, memory-constrained devices you don't own.
The analyze_memory_risk tool addresses this directly. You give it your current device's UDID, your app's bundle ID, and the name of any device profile — including ones you don't own — and it reads your app's current RSS, looks up the Jetsam limit for that profile, and returns a risk badge:
╔════════════════════════════════════════════════════════════════════╗
║ Memory Risk Analysis ║
╠════════════════════════════════════════════════════════════════════╣
║ Device Profile : iPhone 16 Pro (8GB RAM) ║
║ RSS : 820.4 MB ║
║ Jetsam Limit : 4000 MB ║
║ Usage : 20.5% ║
║ ║
║ ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 20.5% ║
║ ║
║ Risk Level : 🟢 SAFE ║
╚════════════════════════════════════════════════════════════════════╝The same app, same session, different profile:
╔════════════════════════════════════════════════════════════════════╗
║ Memory Risk Analysis ║
╠════════════════════════════════════════════════════════════════════╣
║ Device Profile : iPhone SE (2nd/3rd gen) (3GB RAM) ║
║ RSS : 820.4 MB ║
║ Jetsam Limit : 900 MB ║
║ Usage : 91.2% ║
║ ║
║ ██████████████████████████████████████░░ 91.2% ║
║ ║
║ Risk Level : 🔴 CRITICAL ║
╚════════════════════════════════════════════════════════════════════╝If you're above 100% of the limit, the badge reads 💀 WOULD CRASH. That's not a warning — it means your app would already be dead on that device right now, at its current memory footprint. That's the kind of information you want before your users tell you about it in a one-star review.
Four Workflows That Changed How I Debug
Workflow A: Triage a Crash You Didn't See Happen
You wake up and your app has crash reports from overnight. The old approach: open Xcode Organizer, download the crash, try to match the timestamp to log entries you may or may not have captured.
With the MCP server, the whole investigation stays in one conversation. Start with list_devices to get your simulator's UDID, then call list_crashes with your bundle ID and a since timestamp:
list_crashes(
bundle_id="com.example.MyApp",
since="2026-02-18T00:00:00"
)You get a table of crash files sorted newest first, with exception type already visible. Pick the one you care about and call get_crash_detail with its path — you get the exception type, signal, reason, and the crashed-thread backtrace formatted cleanly. If the crash happened on a background thread, add verbose=True to pull all threads and loaded images.
The entire flow — from "my app crashed overnight" to reading the exact backtrace — happens inside a single Claude conversation. No tab switching. Claude can immediately help you interpret the backtrace because it's looking at the same output you are.
Workflow B: Test Against a Constrained Device You Don't Own
You're developing on an iPhone 16 Pro simulator. Your users are on iPhone SE. You ship an update that adds a new image pipeline. It's fine on your machine. It kills the app on theirs.
Before shipping, you can now ask Claude to check:
analyze_memory_risk(
udid="<your-simulator-udid>",
bundle_id="com.example.MyApp",
device_profile="iPhone SE (2nd/3rd gen)"
)This reads your app's current RSS from the running simulator and compares it against the SE's 900 MB Jetsam limit. If you're at 780 MB, Claude will show you a CRITICAL badge and you'll know — before any SE owner downloads your update — that you have a problem to solve.
The device profile table covers everything from iPhone SE 1st gen (350 MB limit) through iPhone 16 Pro Max and iPad Pro M4. You can stress-test against any of them without owning a single physical device.
Workflow C: Does My App Actually Respond to Memory Pressure?
Most iOS apps implement didReceiveMemoryWarning. Fewer actually test whether it does anything useful. The trigger_memory_warning tool fixes that.
Call it with your simulator's UDID and your app's bundle ID, and it fires a critical memory pressure event at the simulator, waits for the app to react, then captures the log response:
App Log Response (after warning)
┌─────────────────────┬───────┬───────────────────────────┬──────────────────────────────────────┐
│ Time │ Level │ Subsystem │ Message │
├─────────────────────┼───────┼───────────────────────────┼──────────────────────────────────────┤
│ 2026-02-18 11:45:03 │ WARN │ com.example.MyApp │ Memory warning received — purging │
│ 2026-02-18 11:45:03 │ INFO │ com.example.MyApp │ ImageCache: freed 142 MB │
│ 2026-02-18 11:45:04 │ INFO │ com.example.MyApp │ DataCache: evicted 23 entries │
└─────────────────────┴───────┴───────────────────────────┴──────────────────────────────────────┘If you see "freed 142 MB" — your memory warning handler is working. If the log after the warning is silent — you have a handler that does nothing, and you'll now catch that in development instead of production. Follow up with another analyze_memory_risk call to confirm the RSS actually dropped.
This is simulator-only (physical devices don't support memory_pressure via the command line), but that covers most development-time testing.
Workflow D: Memory Leak Hunt Across a User Flow
The detect_leaks tool wraps the macOS leaks(1) command into something you can invoke from chat. The workflow is baseline → reproduce → compare:
Call detect_leaks right after launching the app to establish a baseline. If there are no leaks yet, you'll see a clean panel. Then go into the app and reproduce the flow you suspect is leaking — navigate through a screen a few times, load some content, navigate away. Come back to Claude and call detect_leaks again. Now you're comparing call stacks, not guessing.
The output when leaks are found includes the leaked object types sorted by count and up to five full call stacks, so you can see exactly which allocation path is responsible:
╔══════════════════════════════════════════════════╗
║ Leak Detection ║
╠══════════════════════════════════════════════════╣
║ 3 leak(s) — 2.4 KB total leaked ║
╚══════════════════════════════════════════════════╝
Leaked Objects by Type
┌────────────────────────────┬───────┬──────────┐
│ Type │ Count │ Size │
├────────────────────────────┼───────┼──────────┤
│ NSString │ 2 │ 512 B │
│ UIImage │ 1 │ 1.9 KB │
└────────────────────────────┴───────┴──────────┘
╔══════════════════════════════════════════════════╗
║ Stack #1 ║
╠══════════════════════════════════════════════════╣
║ 0. malloc (in libsystem_malloc.dylib) ║
║ 1. -[MyImageLoader loadSync:] +164 ║
║ 2. -[MyViewController viewDidLoad] +88 ║
║ 3. UIKit _performInitialization +440 ║
╚══════════════════════════════════════════════════╝Like trigger_memory_warning, this is simulator-only — the leaks command-line tool doesn't support physical devices.
Installation
Clone the repository, then register it with Claude Code using a single command:
claude mcp add --scope user ios_memory /opt/homebrew/bin/uv -- --directory /path/to/ios-memory-mcp-server run server.pyRestart Claude Code, and the eight tools are immediately available. The server bootstraps its own Python dependencies (mcp, PyYAML, rich) on first run, so there's no separate pip install step.
What Changes When AI Is the Diagnostic Interface
The individual tools are useful. But the real shift is what happens when all the diagnostic context lives in the same conversation as the AI helping you debug.
Claude can chain these tools in ways that a human context-switching between four applications can't easily do. It can observe a high memory reading from analyze_memory_risk and immediately suggest running detect_leaks to see if a retention cycle is responsible. It can read a crash backtrace from get_crash_detail and correlate the timestamp against log entries from read_logs — because both tools ran in the same thread of reasoning.
Memory debugging on iOS has always been technically approachable but ergonomically exhausting. Every tool exists. Getting them to talk to each other, in context, while you're in the middle of a debugging session — that's what was missing. This is an attempt to fix that.
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