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Aperture MCP

by jaysinailabsversion 0.2.0i★ –iRuns on your computer
Registry nameio.github.jaysinailabs/aperture-mcp

Install

Overview

Description from the maker (original English)

Flag when a tracked commitment vanishes verbatim between two versions. A signal, not a judge.

PopNx MCP’s three pillars

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

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).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

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": {
    "aperture": {
      "command": "uvx",
      "args": [
        "aperture-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add aperture -- uvx aperture-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "aperture": {
      "command": "uvx",
      "args": [
        "aperture-mcp"
      ]
    }
  }
}

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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "aperture": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "aperture-mcp"
      ]
    }
  }
}

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.

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 jaysinailabs, 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 32706 servers in this category
  • Published to the registry more recently than 15% of the other 32706 servers in this category
  • Basic checks equal to or better than 100% of the other 32706 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 Aperture MCP 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 4 months 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, Kanal Varna — drains, leaks & plumbing (Varna, BG)

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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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.

Aperture

A commitment tripwire · git hook · CI · CLI · MCP

Did an agent quietly drop a commitment from your spec — and no one noticed?

English · 简体中文

AI agents now rewrite the documents that govern your work — specs, plans, ADRs, charters, AGENTS.md files. Somewhere in the edit, a constraint you set earlier can quietly disappear.

**Aperture is a *commitment tripwire*. You name the commitments you care about; it flags — word for word** — when one of them vanishes between two versions of a decision document.

Catching *"a commitment silently vanished"* is a tripwire job: it should fire on an event, deterministically, without asking permission. So Aperture ships that check on the surface that fits it best first — a git pre-commit hook / CI check that runs with no LLM, offline — and also as a CLI and an MCP server for agents to call mid-task. MCP is one adapter, not the whole product.

A signal, not a judge. It trips; *you* investigate.

Opt-in · runs locally · never trains on your data.


What it is (and what it is not)

Aperture compares two text states of the same decision — an earlier version and a later one — and surfaces a narrow, specific kind of decision drift: when a tracked commitment’s exact text disappeared. One engine, several surfaces:

  • git pre-commit hook / CI check — the deterministic form. Fires on the commit / PR event with no model in the loop, and blocks (or warns) automatically.
  • `aperture check` CLI — run the same check by hand between any two git states.
  • MCP server — so an agent can call the check while it edits (weaker as a tripwire, since it only runs if the agent *chooses* to call it — but useful mid-task).

What the engine does and doesn’t do:

  • ✅ It does: flag when a commitment you listed *verbatim* is present in version A and gone from version B — across commits, sessions, or authors. It returns a structured, comparable result with its own blind spots written on the label.
  • ❌ **It does *not*: understand meaning. It matches text as a case-insensitive substring, so it misses** a commitment that was *reworded / softened / paraphrased* (it looks dropped-free even though the promise weakened); it declines/abstains on a commitment that was merely *translated* (it can’t compare verbatim across scripts, so it returns degraded rather than false-flag); and it can still false-flag a commitment that was merely *reformatted* (the words moved, the meaning didn’t). It does not rank options, score quality, or tell you a change was *wrong*. That judgment stays with you. Moving to the deterministic git-hook makes the check *fire reliably* — it does not widen what it can see. Same narrow, verbatim signal.

If you want one sentence: Aperture is `grep` for vanished commitments, wired to fire on commit — and honest enough to admit what it can’t see.


Quickstart (≈2 minutes)

pip install aperture-mcp   # installs the `aperture` CLI + the MCP server (wire the CLI as a git hook — see below)

The PyPI package is named `aperture-mcp` because the bare name aperture was already taken on

PyPI. The -mcp suffix is a historical package-name artifact — the product is Aperture, and

MCP is only one of its surfaces. One pip install gives you all three below.

1. The deterministic tripwire — git pre-commit hook / CI (no LLM, offline)

Create a .aperture.toml — a watchlist of the commitments that must not silently vanish, per file:

fail_on_drop = true

[[watch]] path = "CHARTER.md" commitments = ["never train on your data", "data stays on the device"]

aperture check compares two git states and flags any watched commitment that disappeared verbatim:

aperture check                                    # HEAD vs working tree (default)

aperture check --staged # HEAD vs the staged index — for a pre-commit hook aperture check --ref-a origin/main --ref-b HEAD # any two refs — for CI on a PR

Exit code 1 blocks the commit when a watched commitment dropped (the default); --warn-only prints the finding but never blocks. It’s stdlib-only, makes no network calls, and runs no model.

Wire it as a pre-commit hook — either through the pre-commit framework (uses this repo’s .pre-commit-hooks.yaml):

# .pre-commit-config.yaml

repos:

  • repo: https://github.com/jaysinailabs/aperture-mcp rev: v0.2.0 hooks:
  • id: aperture-commitment-drift

…or as a standalone .git/hooks/pre-commit:

#!/bin/sh

exec aperture check --staged

…or run it in CI as a GitHub Action on every PR (needs fetch-depth: 0 so both sides are available — see examples/github-action/aperture-check.yml):

- uses: actions/checkout@v4

with: { fetch-depth: 0 }

  • run: pip install aperture-mcp
  • run: aperture check --ref-a ${{ github.event.pull_request.base.sha }} --ref-b ${{ github.sha }}

Kick the tires first with the bundled fixture (clone the repo): it trips on a dropped commitment

against checked-in before/after docs, no setup, fully offline —

python3 examples/git_decision_drift/git_decision_drift.py.

2. Mid-task, from an agent — the MCP server

The same check, callable by an agent while it edits. (An MCP tool only fires if the agent *chooses* to call it — a weaker delivery for a tripwire than the git-hook, but handy mid-task.)

{

"mcpServers": { "aperture-mcp": { "command": "aperture-mcp" } } }

Prefer zero-install? Point the client at uvx instead:

{ "command": "uvx", "args": ["aperture-mcp"] }.

3. Or from your own Python
from aperture import compare, Anchor, AnchorKind

result = compare( state_a="We commit to: ci-gates-green before release; data-never-leaves-device.", state_b="We commit to: data-never-leaves-device.", anchors=[Anchor(kind=AnchorKind.COMMITMENT, id="ci-gates-green")], ) print(result.status) # DROPPED_SILENTLY print(result.anchor_violations) # the commitment that vanished


A scene you’ll recognize

On a long task, your agent keeps rewriting the doc it works from — a plan, a spec — across sessions and edits. And every so often, a line that mattered just… vanishes.

*“Always ask before you delete anything.”* Gone. *“User data never leaves the device.”* Gone. *“The free tier stays free.”* Gone.

Nobody meant to drop them; nobody reads all 400 lines of the diff.

Aperture watches the exact lines you name. Put it on the commit — a hook that fires before the drop lands — and it won’t try to understand the doc or judge it; it just tells you which promise was there, word for word, and now isn’t. A tripwire, not a judge — and honest about the rest: soften a line, reword it, or change a number instead of deleting it, and it’ll slip past. Better you hear that now.


What trips it — and what slips past

Aperture is a heuristic. We measured it on our own gold corpus and we publish the numbers instead of a single flattering score, because *knowing where it’s blind is the product* — recall 0.400, precision 0.667 on a 100-case corpus, labeled by an isolated LLM-judge panel (it catches 26 of 65 real drifts; ~1 flag in 3 is noise), full breakdown in docs/measured-limits.md:

Kind of changeDoes Aperture flag it?
A watched commitment deleted verbatim✅ Reliably — this is the one thing it’s good at (24 of 24 in the corpus)
A commitment reworded / softened (“must” → “should”)❌ Missed — the text still “matches”
A commitment paraphrased / restructured❌ Missed
A number / scope / negation quietly changed❌ Missed
A commitment translated to another language⚠️ Declines (abstains) for a *natural-language* anchor — it can’t compare verbatim across scripts, so it returns degraded rather than false-flag (a commitment dropped and translated is missed)

The deterministic surface doesn’t widen the aperture. The git-hook fires reliably — but it still

only catches verbatim deletion. Every ❌ / ⚠️ row above is exactly as blind through the hook as

through MCP. What you gain is *when* it checks (on the commit, without anyone remembering to ask),

not *what* it can see.

Anchor style matters for that last row: the abstain applies to a natural-language anchor. A

code-identifier anchor (the ci-gates-green style the quickstart teaches) is treated as

*translation-stable* — Aperture keeps checking it across languages, so if that exact token disappears

it still flags DROPPED_SILENTLY (usually what you want for a stable identifier).

Takeaway: treat every flag as *“look here,”* never as *“this is wrong”* — and never assume silence means nothing drifted. Aperture catches the verbatim disappearance case well and is honest that it catches little else. That narrow, reliable signal is useful precisely *because* it doesn’t pretend to be more.

Hit one of those misses on your own docs? That's the single most useful thing you can send us —

report it in ~30s (your wording is optional). Real misses guide what

we fix next.

Why not just git diff / grep? You can reproduce the core check by hand. What Aperture adds is

that it’s wired to fire on the commit / PR event (as a hook or CI check) *and* callable

mid-task by an agent (over MCP); it returns a structured, directional result

(ok / degraded / DROPPED_SILENTLY / …); and it reports its own blind spots in the result

so a human can audit the gaps. It’s ergonomics + honesty around a simple, legible check — not a

smarter detector.


Why this exists

Long-running and multi-agent workflows drift. A constraint set in turn 3 / session 1 / by agent A gets quietly edited away forty turns later, in another session, by agent B — and nobody notices until it ships. Aperture is a preflight you can put on the documents agents maintain: name the commitments that must not silently vanish, and get a tripwire when one does — ideally on the commit itself, before the drop ever lands.

It is deliberately small and legible. It is not an AI that decides for you; it is a signal that helps *you* stay consistent with yourself.


Who it’s for

Teams and builders who (a) let AI agents edit repo-resident decision documents — specs, plans, ADRs, charters, and AGENTS.md files — and (b) keep those documents under version control. If your agents touch text that encodes promises, Aperture gives you a cheap, honest tripwire — on the commit, in CI, or mid-task — on the ones you can’t afford to lose silently.


Privacy

  • Opt-in and local. Aperture runs on your machine — the git-hook, the CLI, and the MCP server alike. It makes no network calls.
  • Never trains on your data. Your decision text is yours; it never leaves your process.
  • Usage logging is off by default and, when enabled, records only metadata (timestamp, tool, status, counts) — never your decision text or commitment wording.

Honesty about the demo

The repository ships a small hand-authored fixture ADR (a before/after pair under examples/git_decision_drift/fixtures/), where Aperture corre

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

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