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Category
AI Tools
Rank
No. 1167Tools index

Previous survey · No. 1155 ·

Pricing
Open Source
Type
APP
Use case
Data, Retrieval & Knowledge
Interfaces
Desktop · MCP
GitHub
207 stars
Latest release
v1.1.5
Date

About

A macOS menu bar app that silently records the text of whatever window is focused throughout the day and writes it to local markdown files, then uses an agent CLI (Claude Code, Codex, opencode) to turn that record into a cited knowledge base and daily summary. It exposes an MCP server with twenty tools so AI assistants can query the record, knowledge, and notes directly.

What it does

Ambient Context is a macOS menu bar utility that quietly captures the text on screen as you work, reading whichever window has focus every few seconds through the operating system's accessibility layer. It writes what it sees to plain text files, one set per day, with no screenshots, video or audio involved. On a schedule or on demand, an AI coding assistant you already run turns that day's file into a short cited page and a written summary. The same file is also reachable through MCP, the protocol AI assistants use to call outside tools, so a model can look up what actually happened on a given day.

Why it's ranked here

This is a single developer's project that treats a sensitive capability, reading everything on your screen, with real discipline. The privacy documentation separates what is structurally guaranteed from what is a heuristic, and states its own known gaps instead of hiding them. The source carries unit tests for the parts most likely to break silently: protocol dispatch and the window-snapshot parser. It is MIT licensed, needs no account, and sends nothing anywhere except an optional update check. The cost is dependency: it only runs on Apple Silicon Macs, and half its value, the knowledge base and daily notes, needs an external agent CLI you must already own and configure.

What's good

The redaction model works in layers. Sensitive input fields are skipped before their text is read, whole snapshots are dropped for password managers and private browsing windows, and pattern matching scrubs credential-shaped strings afterward. Screen text is deduplicated across the day, so a page you keep revisiting is recorded once. Captured blocks keep the actual file path or web address behind them when the source app exposes one, so a reader can open the original document instead of trusting a scraped fragment. Read access keeps working even when the app is closed, while anything that changes settings or capture state gets logged with the connecting client's name attached.

Tradeoffs

Capture quality is uneven by design. Chromium and Electron based apps, browsers, chat clients and editors among them, build their accessibility structure lazily, so the first seconds after switching to one are thin and only fill in on later passes. GPU rendered terminal emulators expose little or no readable text at all, so a terminal heavy workflow will show real gaps in the record. The documentation is explicit that redaction is a heuristic, not a guarantee: an ordinary, non private browser tab showing banking, health or mail content is captured like any other page, and an app that mislabels a secure field defeats the strongest filter available.

How to use it well

This suits someone who runs an agent CLI day to day and wants it working from what actually happened, not a hurried recap. Point the capture folder outside cloud synced directories to keep the record on one machine, grant the permission it asks for on first launch, and set a processing time so the daily page and summary are ready each morning. Install the bundled skills so the assistant knows when to pull a summary rather than read everything. It is a poor fit if your day lives mostly in a GPU rendered terminal, or you are not ready to treat a folder of your own screen text as sensitive.

Technical notes+

The desktop app and the MCP server are the same Tauri binary: src-tauri/src/main.rs checks for an mcp argument and, if present, runs src-tauri/src/mcp/mod.rs's stdio JSON-RPC loop directly rather than launching Tauri's window and event loop, so the server can run as a bare subprocess with no app window ever open. That module negotiates protocol version 2025-11-25 with two older fallbacks and unit tests its own dispatch table, including that an unsupported protocol version falls back cleanly and that no response line ever contains a raw newline, which would break stdio framing. src-tauri/src/reader/mod.rs defines a platform independent Snapshot type and delegates the actual accessibility walk to a macOS specific implementation, with a Windows module present behind the same trait despite the README and install instructions covering macOS only. Folder and settings changes in src-tauri/src/lib.rs route through a save_settings_recorded helper that writes to the day's ledger, and src/App.tsx is a single component that switches between three views using a URL query parameter rather than a router. The bundled skill at skills/ambient-context-catch-me-up/SKILL.md hardcodes a five step read order, list the days, read today's summary, read yesterday's before 10am, then the knowledge base, before it falls back to reading raw day files.

Observed

License
MIT (bundled fonts under a separate SIL Open Font License)
Platform
macOS 14 or later, Apple Silicon only
Packaging
Native desktop app built with Tauri: a Rust core and a React and TypeScript frontend, built via Node and Cargo
Interfaces
Menu bar GUI and an MCP server reachable over stdio, from the same compiled binary
Network surface
No account and no telemetry; the only outbound call is an optional GitHub Releases update check
Test coverage
Unit tests are present in the Rust source for the MCP protocol dispatch and the window-snapshot parser

Read from README.md, package.json, src/App.tsx, src/main.tsx, src-tauri/src/lib.rs, src-tauri/src/main.rs, src-tauri/src/mcp/mod.rs, src-tauri/src/reader/mod.rs, docs/day-context-prompt.md, docs/privacy-and-security.md, docs/skills.md, skills/ambient-context-catch-me-up/SKILL.md, LICENSE.

What it can do

  • Record on-screen text of the currently focused window

    Focused window content via macOS accessibility API → Text record

  • Write captured text into local markdown files

    Captured on-screen text → Markdown files

  • Generate a cited knowledge base from the recorded text using an agent CLI

    Recorded markdown files → Cited knowledge base

  • Generate a daily summary from the recorded text using an agent CLI

    Recorded markdown files → Daily summary

  • Expose an MCP server with tools for AI assistants to query the record, knowledge, and notes

    Query from AI assistant → Record/knowledge/notes data

Tags

mcpcontext-capturemenu-bar-appmacosclaude-codeknowledge-baseprivacy

Tech Stack

Node.jsTypeScriptVite

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Editorially curated, with community endorsements as a secondary signal. Corrections welcome.