Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Beschreibung vom Entwickler (englisches Original)
Invisible per-recipient image watermarks. Mark one copy per recipient, then identify a leaked copy.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"faintmark": {
"command": "uvx",
"args": [
"faintmark",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add faintmark -- uvx faintmark mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"faintmark": {
"command": "uvx",
"args": [
"faintmark",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"faintmark": {
"type": "stdio",
"command": "uvx",
"args": [
"faintmark",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto matchalatteguy bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.1.0) kam vor 27 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Figma Context MCP, Claude Real Video, Figma MCP Server
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
Let any LLM watch a video locally — and search everything it has ever watched.
The Figma MCP server brings Figma design context directly into your AI workflow.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
Invisible per-recipient image watermarks. Create one marked copy per recipient, then identify which copy a shared or leaked image is. Designed to tolerate the recompression and resizing chat apps apply; see the app tests and limits below. Runs locally, no account, no upload. Python CLI and MCP server.
$ faintmark mark photo.jpg alice bob
alice 991e318a21 /Users/me/Pictures/marked/photo-991e318a21.jpg bob 746ac9fa88 /Users/me/Pictures/marked/photo-746ac9fa88.jpg 2 copies in /Users/me/Pictures/marked
$ faintmark check ~/Downloads/forwarded.jpg matched: alice 991e318a21 marked 2026-10-06T23:11:54Z
Send each person only their own file; file names carry the id, not the name. A match identifies the label the copy was issued under, not who shared it. The mark is intended to be visually unobtrusive on photographs. JPEG, PNG and WebP keep their format; HEIC becomes JPEG.
uvx faintmark mark photo.jpg alice bob # runs without installing; needs uv
uv tool install faintmark # or: pipx install faintmark
Python 3.11 or newer on macOS, Linux or Windows (Intel Macs: 3.11 to 3.13). uv can install Python and run FaintMark. The first mark or check downloads Adobe's TrustMark Q models (65 MB, SHA-256 verified) into ~/.faintmark. For HEIC input install faintmark[heic]: uv tool install 'faintmark[heic]', or without a persistent install uvx --from 'faintmark[heic]' faintmark mark photo.heic alice. uv tool uninstall faintmark removes the command and leaves ~/.faintmark in place.
faintmark mark IMAGE RECIPIENT... [-o DIR] [--format png|jpeg|webp] [--json]
faintmark check IMAGE [--deep] [--files] [--json] faintmark list [-n N] [--files] [--json]
Copies go into marked/ beside the image unless -o says otherwise. Quote labels with spaces: faintmark mark photo.jpg 'Alice Smith'. One run marks 1 to 100 recipients. --json prints the result the matching MCP tool returns; mark includes the source file name and output paths, check and list omit file names and paths unless --files is set. check --deep also tries a flip and small rotations, and when nothing is detected lists up to three registered ids within 26 of 96 codeword bits of the raw decoder output as candidates, with their bit distance. Those are uncalibrated and can be coincidental or forged; they are never a match. With --json, errors print {"error": "..."}. check exits 0 for matched, 1 for unregistered or not_detected, 2 for an error.
mark accepts non-animated JPEG, PNG, WebP and HEIC up to 25 megapixels without transparency (16-bit RGB is read at 8 bits; 16-bit grayscale and CMYK are rejected) and needs about 1.5 GB of memory at that size. check reads the first frame of anything Pillow opens, up to 100 megapixels.
claude mcp add --scope user faintmark -- uvx faintmark mcp
codex mcp add faintmark -- uvx faintmark mcp gemini mcp add -s user faintmark uvx faintmark mcp
Any other client that reads an mcpServers config (Claude Desktop, Cursor, Windsurf): add this entry to the client's MCP configuration. The client must find uvx on its PATH; set FAINTMARK_HOME in the server's environment to use another registry.
{ "mcpServers": { "faintmark": { "type": "stdio", "command": "uvx", "args": ["faintmark", "mcp"] } } }
Choose one route per client; plugins and extensions bundle the skill too. For HEIC input start the server as uvx --from 'faintmark[heic]' faintmark mcp; the bundled launchers use the base package.
claude plugin marketplace add matchalatteguy/FaintMark then claude plugin install faintmark@faintmarkcodex plugin marketplace add matchalatteguy/FaintMark then codex plugin add faintmark@faintmarkgemini extensions install https://github.com/matchalatteguy/FaintMarknpx skills add matchalatteguy/FaintMark| Tool | Arguments | Returns |
|---|---|---|
mark_image | image_path, recipients, optional output_dir, format | output folder and each copy's id, path, warnings |
check_image | image_path, optional files, deep | status of matched (with recipient, id, marked_at, and same_image when a stored hash exists), unregistered (with id) or not_detected; with deep, transform naming the flip or rotation that recovered the result, and suspects (id, recipient, bit_errors, same_image) plus registry_size when nothing was detected |
list_copies | optional limit, files | registered copies, newest first |
Give tools absolute paths. unregistered means an id was decoded but it is not in this machine's registry; not_detected means no readable mark, which says nothing about where the image came from. same_image compares perceptual hashes with the issued copy: false means they differ beyond the threshold, which cropping or an id copied into another picture can cause; the comparison can also miss differences.
check_image and faintmark check --json return one of these: the three plain statuses (a match with same_image: false means the picture differs from the issued copy); with deep, a result recovered by a transform, or a miss with candidates (suspects is empty when no registered id is within the cutoff). The CLI prints errors as the last line; MCP tools raise them as tool errors (isError): messages from the service match, argument validation messages differ.
{"status": "matched", "id": "991e318a21", "recipient": "alice", "marked_at": "2026-10-06T23:11:54Z", "same_image": true}
{"status": "matched", "id": "991e318a21", "recipient": "alice", "marked_at": "2026-10-06T23:11:54Z", "same_image": false} {"status": "unregistered", "id": "3123f6f877"} {"status": "not_detected"} {"status": "matched", "id": "991e318a21", "recipient": "alice", "marked_at": "2026-10-06T23:11:54Z", "same_image": true, "transform": "rotated 4"} {"status": "not_detected", "suspects": [{"id": "991e318a21", "recipient": "alice", "bit_errors": 21, "same_image": true}], "registry_size": 100} {"error": "forwarded.jpg: no such file"}
mark_image and faintmark mark --json return the first line; list_copies and faintmark list --json return the second, newest first:
{"original": "photo.jpg", "output_dir": "/Users/me/Pictures/marked", "copies": [{"id": "991e318a21", "recipient": "alice", "path": "/Users/me/Pictures/marked/photo-991e318a21.jpg", "warnings": []}]}
{"copies": [{"id": "991e318a21", "recipient": "alice", "marked_at": "2026-10-06T23:11:54Z", "warnings": []}]}
7 October 2026, FaintMark 0.1.0, three source photos (an iPhone scene, a CC0 portrait, a CC0 sunset). Each row reports the samples actually sent on that route at default photo quality. Screenshot rows are screenshots of the receiving phone, cropped to the photo before checking. These are individual round-trip observations, not recovery rates or guarantees for an app.
| Route | Received | Result |
|---|---|---|
| WhatsApp photo, all three | long edge 1600 px, 155 to 317 KB | matched 3/3 |
| Discord attachment, scene | original size, 1.7 MB | matched 1/1 |
| Discord sticker made from the scene | 217x285 PNG | matched 1/1 |
| Telegram photo, scene | 960x1280, 102 KB | matched 1/1 |
| WeChat photo, portrait and sunset | long edge 1644 to 1920 px, 67 to 302 KB | matched 2/2 |
| Instagram DM, portrait | 1206x804, 176 KB | matched 1/1 |
| Chat screenshots on WhatsApp, Discord, Telegram, WeChat, Instagram | photo long edge 286 to 1022 px | matched 9/9 |
| Tinder profile card, portrait (screenshot) | heavy crop | not detected 0/1 |
Every match also reported same_image: true. The originals, every received file, the screenshots, the crops and a manifest of expected ids and results (docs/evidence-2026-10-07.json) are in the evidence release. On a machine without this registry, the received files return unregistered with the id from the manifest; the originals and the Tinder crop return not_detected. X, Signal, iMessage and Instagram stories were not tested.
TrustMark Q (Adobe, MIT) embeds a 40-bit id as an image-wide pattern designed to tolerate JPEG recompression, WebP conversion and moderate resizing. This is forensic watermarking, not steganography: anyone with TrustMark can read the id; only your registry maps it to a name. FaintMark draws a random id per recipient, writes the copy in the source format (JPEG at the source's own quantization tables, PNG, WebP; HEIC becomes JPEG), decodes the saved copy to verify its id is recoverable, and records id, recipient, file and a perceptual hash of the copy in a local SQLite registry. On a registered match, check compares perceptual hashes and reports same_image: false when their distance exceeds the threshold. A JPEG whose source settings lose the mark is saved at quality 95 instead, and says so in its warnings. Marking an image that already carries a mark replaces it and says so. A preflight re-encodes each copy at JPEG quality 70, at full size (skipped with a warning when a side exceeds 65,500 px, JPEG's limit) and resized to 1280 px (half size when the image is smaller), and warns when the id does not survive; send those copies as a file rather than a photo.
ICC profiles are kept so Display P3 photos stay the same colour. EXIF and other metadata are not copied.
Compatibility with Adobe's implementation is checked in: tests/fixtures holds BCH vectors and two images marked by upstream TrustMark 0.9.2; FaintMark decodes their ids and reproduces their marked pixels within one value per RGB channel. A chat-app style ladder in the real-model tests (resize to 1600, 1280 and 1024 px with JPEG 70 to 80, WebP 75, and a chained resize-plus-JPEG pass, on delivered JPEG bytes) recovers the id on two 12-megapixel synthetic covers and the 320 px fixture, except WebP 75 on the fixture.
Someone who has this repository and a copy can do the following; measured on three photos at 2000 px, two ids each, full tables in the attack report attached to the evidence release.
check then says not_detected, exactly as for an unmarked photo.check --deep lists at least one colluder in 253 ofNur der Anfang wird gezeigt; den Rest findest du im Repo