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Simframe

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
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Always-warm iOS Simulator and Android emulator frames: agents read the screen in ~20ms

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet β€” we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself β€” its README, its docs, or a verified owner. We haven’t found those for simframe, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

simframe

ci npm license Glama score

Eyes, hands and memory for a coding agent driving the iOS Simulator or an Android emulator. The agent reads the screen as text with tap points, runs a whole flow in one call, and remembers screens it has seen β€” so it stops paying a screenshot, a model round trip and a re-read for every single step.

simframe driving Settings on an iPhone 17 Pro simulator: one call, four verified steps, no screenshots, and the screen returned as text with tap points

Install in ten seconds

You need a Mac with Xcode (you have one if you have a simulator) and Node 18+. Nothing else β€” no idb, no Appium, no Python.

Claude Code β€” as a plugin, which brings the MCP server and the skill that teaches the protocol in one install:

Terminal
claude plugin marketplace add lvlrSajjad/simframe
claude plugin install simframe@simframe

or the MCP server alone:

Terminal
claude mcp add --scope user simframe -- npx -y simframe mcp

Pick one, not both: two installs mean two servers driving the same device. If you added it with claude mcp add before, claude mcp remove simframe first.

Claude Desktop β€” Settings β†’ Developer β†’ Edit Config, then add:

config.json
{
  "mcpServers": {
    "simframe": { "command": "npx", "args": ["-y", "simframe", "mcp"] }
  }
}

Cursor β€” Install in Cursor or add the same simframe block to ~/.cursor/mcp.json.

Codex, Windsurf, Zed, VS Code, anything else that speaks MCP β€” the same JSON block, wherever that client keeps its mcpServers.

Then boot a simulator and ask the agent for something: "open my app and tap through the signup flow", "is the list on the Orders tab loading?", "why does the Save button do nothing?". The first call builds a small Swift daemon from source β€” about 15 seconds, once.

The CLI is the same thing without an agent in the loop, and the cheapest way to check the install:

Terminal
npm install -g simframe
simframe doctor          # every layer, and which engine carries it
simframe ui              # the current screen as a numbered text map

If more than one simulator is booted, name the one you mean with --device=<udid> or export SIMFRAME_DEVICE=<udid>; simframe refuses to guess, because the command that would act on the wrong one is a tap.

What it changes

An agent driving a simulator is slow for three reasons, and only the first is obvious. Every look is a wait (simctl io screenshot costs ~130 ms, paid on every glance). Every step is a model round trip. And nothing is remembered, so the same screen is re-read and re-reasoned about each time it appears. There is a fourth that is pure waste: an image is the most expensive way to ask what is on screen, and it still does not say what is tappable.

Without simframeWith simframe
Look at the screen~130–400 ms screenshot, blocking~20 ms, already captured
"Did anything change?"a full image~2 ms, text only
Reading a screenan image, ~1,600 tokens, no tap points~330 tokens of text, with tap points
A 10-step flow10 turns, 10 images1 turn, 0 images
Same flow, againevery run is the firstreplayed from memory, every step verified, zero model calls

Measured on an iPhone 17 Pro simulator, iOS 26.5, Apple Silicon, against a real production app; the full tables with N, median and p95 are in docs/BENCHMARKS.md.

This is what the agent gets back instead of a picture:

Code
iPhone 17 Pro Β· 402x874pt Β· screen a1b2c3d4 "Inbox" (known, 3 known exits)
last action: [2] tap β€” ok: matches the outcome seen 5x before
nav-bar:
  #1 button    24,64      Back
  #2 text      201,64     Inbox
content:
  #3 cell      201,140    Weekly digest
  #4 field     201,196    Search = weekly ~ weekly|
  #5 switch    201,252    Notifications = 1
tab-bar:
  #6 text      62,835     Inbox
  #7 text      201,835    Settings
next: settled; nothing ambiguous β€” chain the next steps in one sim_do without looking again.

Region, type, label, contents, a tap point, and a number the next call can use as a selector. = value is what the accessibility tree says the control contains; ~ value is what OCR read off the pixels. Where they disagree, that is the point.

The tools

ToolWhat it does
sim_uiStart here. The screen as the numbered text map above.
sim_doThe main tool. A whole flow in one call β€” tap, type, scroll, wait, assert β€” each step settling before the next and verified against what it did last time. Fallback selectors, optional steps, and a bounded seek let a batch survive a surprise instead of handing it back.
sim_stateHas anything changed since your last look, and which regions moved.
sim_goto Β· sim_flow_runWalk to a screen simframe has been to before; replay a saved flow with zero model calls.
sim_find Β· sim_tap Β· sim_type_into Β· sim_scroll_to Β· sim_wait_for Β· sim_assertSingle actions, for when you genuinely only have one step.
sim_launch Β· sim_open_url Β· sim_permissionLaunch (confirmed to have reached the front), open a deep link, grant a privacy permission without tapping the alert.
sim_lookThe only tool that returns an image, capped at 1024 px β€” for layout, colour and spacing.
sim_recall Β· sim_strip Β· sim_storageWhat happened in the last minute, as text or as a contact sheet; what the app saved (UserDefaults, AsyncStorage), even on a device that is shut down.
sim_wait Β· sim_capture Β· sim_devicesWait for change then settle; manage capture; list simulators and emulators.

Three ways to name a control, anywhere one is named: "Save" (resolved by intent β€” verbs, typos, synonyms, icon-only controls by their common name), #3 (the number the map gave it, valid for that screen only), or @120,400 (raw points, last resort). A label that matches the destructive vocabulary β€” Delete, Pay, Send, Sign out β€” is never substituted for or guessed at.

A Claude Code skill ships in the package and teaches the CLI path, which is cheaper still: a few lines of output instead of a tool result, and no MCP schema in context until a tool is used.

Android

The second backend drives an Android emulator with the same commands, the same screen map and the same memory. Frames arrive at ~41 ms through the emulator console; tap, swipe, text, keys and the clipboard work; simframe devices lists emulators next to simulators. What Android does not have is an accessibility tree, so its screens are read by OCR and CV alone β€” and simframe doctor says so rather than pretending. Details, costs and the reasoning are in the guide.

How it works, briefly

One Swift daemon per device reads the simulator's framebuffer straight off its IOSurface when the display reports damage, runs Apple's Vision OCR on the same surface, reads the accessibility tree host-side, and sends taps as real down/move/up sequences over the HID channel β€” no PNG, no file, no spawned process. Frames land in ~/.simframe/<udid>/, input goes over a 0600 Unix socket, and the MCP server and CLI are thin clients over both.

Screen memory keys a merged element map by a structural fingerprint, so a screen seen before is a file read. A transition graph records which action led from which screen to which, so sim_goto plans a route and a saved flow replays with no model in the loop.

Every layer degrades rather than fails, and never quietly: simframe doctor reports each one and --strict turns any downgrade into a non-zero exit. The private frameworks the daemon links are documented, with evidence, in docs/PRIVATE_API.md.

Read on

docs/GUIDE.mdthe full guide: recovering without a round trip, screen memory and the two hashes, navigating by memory, the CLI, diagnosing a wedged simulator, keeping a session cheap, the roadmap
docs/BENCHMARKS.mdevery number, with N, median, p95, machine and the measurement traps
docs/EXPERIMENTS.mdwhat we believed before measuring, and the fourteen times it was wrong
docs/DECISIONS.mdthe judgements that changed the plan, including a phase cancelled by its own measurement
docs/DEFERRED.mdevery known defect, open or closed, with the evidence
docs/ARTICLE.mdAgents shouldn't blink β€” the argument the whole thing adds up to

Almost everything here was decided by a measurement rather than an argument, and several measurements reversed a decision that had already shipped. Two headline results: a warm transition graph removes about 27% of an agent's model round trips, and a one-line threshold on a number the daemon already computes beat every local model tried as a step supervisor β€” 95% against 77–91%, at zero latency.

Limitations

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Simframe

We don't have a confirmed install command for simframe yet, so we don't publish a generated one β€” a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/lvlrSajjad/simframe) for the current steps.

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
Last updatedSep 28, 2026
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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