Every App Is Open Source

Name: Every App Is Open Source
Author: thomasisalwayslate

by thomasisalwayslateversion 0.2.0i★ –iRuns on your computer

Registry nameio.github.thomasisalwayslate/every-app-is-open-source

Install

Overview

Description from the maker (original English)

Native reverse engineering with Ghidra and evidence on Apple Silicon macOS. Setup required.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: EAOS_RUNTIME_DIR

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "every-app-is-open-source": {
      "command": "uvx",
      "args": [
        "every-app-is-open-source",
        "mcp"
      ],
      "env": {
        "EAOS_RUNTIME_DIR": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add every-app-is-open-source --env EAOS_RUNTIME_DIR=your-value -- uvx every-app-is-open-source mcp

Fill in the empty values before running it.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "every-app-is-open-source": {
      "command": "uvx",
      "args": [
        "every-app-is-open-source",
        "mcp"
      ],
      "env": {
        "EAOS_RUNTIME_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "every-app-is-open-source": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "every-app-is-open-source",
        "mcp"
      ],
      "env": {
        "EAOS_RUNTIME_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

EAOS_RUNTIME_DIR env
Optional custom Java/Ghidra directory. Use the same value when running eaos setup.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account thomasisalwayslate, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 18336 servers in this category
Published to the registry more recently than 75% of the other 18336 servers in this category
Basic checks equal to or better than 100% of the other 18336 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Every App Is Open Source safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.0) reached the registry 14 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, NexusWindows

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Every App Is Open Source

Give your AI agent a compiled program, and it works out how it's built. Ghidra does the decompiling. This is the agent's side of it: a small set of MCP tools, short answers by default, and evidence you can trace back to the binary.

The name is a provocation, not a license. Seeing how an app works doesn't give you the right to copy it. Read the disclaimer.

What it looks like

An agent connected only to EAOS was given a small compiled program and asked what quote(6) returns. These are its actual tool calls:

Tool call	What came back
`search {"kind":"functions","pattern":"quote"}`	`_quote` at `0x100000460`
`inspect {"addresses":["100000460"],"mode":"all"}`	Assembly and decompiled code, saved as evidence `fe8589bb…`
`read_evidence {"id":"fe8589bb…"}`	The saved evidence, hash-checked

Its answer: 45. Below 4 the function returns 19, otherwise 7*n + 3, citing the address and the evidence ID.

This is one recorded session on version 0.1.1 with an already-analyzed cache, not a benchmark. The full transcript has every call and its limits.

How it compares

I've compared it with REA and GhidraMCP on three modified programs, with the same model and questions for each tool. The results depend on the target, and EAOS doesn't win every time:

Target	EAOS version	vs GhidraMCP	vs REA
Modified Doom	0.1.0	16% cheaper, 17% faster; all 18/18 correct	36% cheaper, 27% faster; all 18/18 correct
Modified Ollama	0.1.4	28% more expensive, 23% slower; GhidraMCP missed 3 of 195 cases	14% cheaper, 17% faster; both 195/195
Modified Ollama, round 2	0.1.5	6% cheaper, similar time	25% cheaper, similar time

Costs are API-equivalent model costs; timings exclude initial analysis. Each row is three runs per tool, so these are descriptive results, not a general ranking. In round 2 the other tools' missed cases were mostly header order and incomplete error text, not wrong behavior. The modified programs and answer keys are private so the tests can't be spoiled. That means you can read every run summary, scoring review and protocol, but you can't replay these exact comparisons yourself. Every number, loss and limitation is in BENCHMARKS.md.

Get your first result

These instructions target 0.2.0. Before publication, use the reviewed local wheel path in place of the package requirement.

You need an Apple Silicon Mac, uv and Apple's Command Line Tools (xcode-select --install). Windows, Linux and Intel Macs aren't supported yet.

1. Install. This downloads checksum-pinned Java and Ghidra (about 770 MB) and builds Ghidra's Apple Silicon decompiler, which the official download doesn't include:

uvx --python 3.13 --from every-app-is-open-source==0.2.0 eaos setup

Everything goes into ~/Library/Application Support/Every App Is Open Source/. It doesn't touch your shell profile, your system Java or your agent's settings. Versions, sizes, troubleshooting and uninstall are in SETUP.md.

2. Build a tiny test program and analyze it:

mkdir -p /tmp/eaos-example

cat > /tmp/eaos-example/example.c <<'C' #include attribute((noinline)) int quote(int n) { return n < 4 ? 19 : n * 7 + 3; } int main(int argc, char **argv) { puts("eaos fixture anchor"); return quote(argc); } C clang -O0 /tmp/eaos-example/example.c -o /tmp/eaos-example/example uvx --python 3.13 --from every-app-is-open-source==0.2.0 eaos call /tmp/eaos-example/example open_program

You should see classification: computed, an ARM64 language, a SHA-256 and an evidence ID.

3. Connect your agent. For Claude Code:

claude mcp add --transport stdio --scope local eaos -- uvx --python 3.13 --from every-app-is-open-source==0.2.0 eaos mcp /tmp/eaos-example/example

Codex and other MCP clients are in CLIENTS.md.

4. Ask:

Find the quote function. What does quote(6) return? Explain both branches and cite the function address and saved evidence.

The correct answer is 45. This checks that your setup works; it doesn't prove the agent will understand any app. Addresses depend on the compiler, so yours will differ from the example above.

For a standalone program, use the same eaos mcp /absolute/path/to/program command. For an application directory, select its root with an explicit external workspace:

uvx --python 3.13 --from every-app-is-open-source==0.2.0 eaos mcp /Applications/Example.app --workspace /absolute/path/outside-bundles/eaos-workspace

Ask the agent to inventory the root, select one native component by its returned ID, then inspect that component. Full native analysis remains the default; quick mode is opt-in. Individual executable paths inside bundles are still refused; copy the executable out for standalone analysis. For big standalone programs, terminal pre-analysis can finish before you connect your agent.

The tools

I started with seven tools based on the calls agents made across 24 earlier test runs. Native-file sessions now expose eight, including bounded static call traversal. Application-directory sessions expose eleven, adding inventory, explicit component selection and bounded JavaScript inspection.

Tool	What it does
`open_program`	What the program is: architecture, memory layout, analysis status
`search`	Find text inside the program and where it's used, or find functions
`inspect`	Decompiled code, raw instructions, callers and callees
`read_memory`	Read static memory or Go string tables, with provenance
`calculate`	Safe arithmetic and byte decoding, so the agent doesn't do math in its head
`decode_swift_string`	Recover text hidden inside Swift code on Apple platforms
`read_evidence`	Pull up the full saved evidence behind any reply, hash-checked
`trace_calls`	Follow bounded static caller/callee relationships, retaining indirect edges and cutoffs as unresolved

Application-directory sessions also expose:

Tool	What it does
`inventory_application`	Inventory a selected root and supported ASAR members, with explicit gaps and budgets
`select_component`	Capture and select one inventoried native component outside the original bundle
`inspect_javascript`	Inspect captured JavaScript source; development profiles add focused preload/module inspection and bounded evidence relationships

0.2.0 extends the initial JavaScript/ASAR inspection from 0.1.9, which was limited to 1 MiB of source.

The 0.2.0 source profiles accept coherent JavaScript captures up to 8 MiB, with explicit bounded syntax-index continuation and fixed result pages. Focus one observed preload API, inspect one selected module or its surrounding loader skeleton, then compose selected saved evidence into a compact source relationship pack. Every candidate retains its component, source, table and evidence identity; unknown bindings remain explicit. Python parser dependencies come with the package; no JavaScript or application is executed.

On official Electron Fiddle 0.39.0, the recorded development check inspected its large bundles and produced 60 selected source nodes, six conditional module-lookup candidates and three preserved narrow preload relationships. It established no complete main-process IPC edge: dynamic factory replacement, loader/interop identity, inherited dispatch and process context remain unresolved. These are local static checks, not a comparative benchmark or a test of reliable agent answers. A separate bounded Ghostty trace still did not establish the complete Swift/C/Zig reload chain. Full Electron support, reconstructed applications and REA parity are not established.

Native traversal also retains available names for unprepared quick-mode candidates. That does not prepare their instruction bodies or establish missing relationships. Full native analysis remains the default.

The exact inputs, outputs and limits of each one are in the tool contract.

Every tool result says what it is

Each tool result is labelled:

fact: read straight from the captured binary or source
computed: worked out from it, like decompiled code or a decoded string
guess: an inference that hasn't been verified

Decompiled code is computed, not the original source. Successful analysis results include saved evidence you can pull up with read_evidence. Status responses and errors don't include evidence IDs; a tool failure makes no analysis claim.

These labels describe the tool results, not your agent's final answer. An agent can still misread correctly labelled evidence. The labels and evidence IDs let you check a conclusion; they don't certify it.

Analyze once, reuse it

Ghidra's analysis is slow, so each standalone program gets its own eaos-analysis/ / folder right next to it, with the analyzed project and evidence. Reopening a compatible current-version profile reuses it; different engine, architecture or analysis profiles can require fresh analysis. Application-directory captures and native caches go into the explicit external workspace. Projects made before 0.1.7 are kept but not reused: 0.1.7 analyzes the program again in its new cache format.

Each session works on a read-only copy of the exact bytes it hashed, so changing the original file doesn't affect a session that's already open. If a cached project doesn't match the program's hash, it stops instead of guessing, and it never deletes old evidence.

One Ghidra quirk: it won't create proje

Only the beginning is shown; read the rest in the repo

Every App Is Open Source

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

NexusWindows

README from the maker

Every App Is Open Source

What it looks like

How it compares

Get your first result

The tools

Every tool result says what it is

Analyze once, reuse it