Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
제작자가 쓴 설명(영어 원문)
Persistent memory for visual intelligence. A photo registry: analyze once, look up free after that.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
PHOTOTOLOGY_API_KEY, PHOTOTOLOGY_BASE_URL, PHOTOTOLOGY_MCP_NO_TELEMETRY, DO_NOT_TRACK, PHOTOTOLOGY_MCP_ALLOWED_DIRS비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"phototology": {
"command": "npx",
"args": [
"-y",
"@phototology/mcp"
],
"env": {
"PHOTOTOLOGY_API_KEY": "your-value",
"PHOTOTOLOGY_BASE_URL": "your-value",
"PHOTOTOLOGY_MCP_NO_TELEMETRY": "your-value",
"DO_NOT_TRACK": "your-value",
"PHOTOTOLOGY_MCP_ALLOWED_DIRS": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add phototology --env PHOTOTOLOGY_API_KEY=your-value --env PHOTOTOLOGY_BASE_URL=your-value --env PHOTOTOLOGY_MCP_NO_TELEMETRY=your-value --env DO_NOT_TRACK=your-value --env PHOTOTOLOGY_MCP_ALLOWED_DIRS=your-value -- npx -y @phototology/mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"phototology": {
"command": "npx",
"args": [
"-y",
"@phototology/mcp"
],
"env": {
"PHOTOTOLOGY_API_KEY": "your-value",
"PHOTOTOLOGY_BASE_URL": "your-value",
"PHOTOTOLOGY_MCP_NO_TELEMETRY": "your-value",
"DO_NOT_TRACK": "your-value",
"PHOTOTOLOGY_MCP_ALLOWED_DIRS": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"phototology": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@phototology/mcp"
],
"env": {
"PHOTOTOLOGY_API_KEY": "your-value",
"PHOTOTOLOGY_BASE_URL": "your-value",
"PHOTOTOLOGY_MCP_NO_TELEMETRY": "your-value",
"DO_NOT_TRACK": "your-value",
"PHOTOTOLOGY_MCP_ALLOWED_DIRS": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
PHOTOTOLOGY_BASE_URL envPHOTOTOLOGY_MCP_NO_TELEMETRY envDO_NOT_TRACK envPHOTOTOLOGY_MCP_ALLOWED_DIRS env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 10일 점검
레지스트리 이름이 도메인 phototology.com 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: PHOTOTOLOGY_API_KEY
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(2.0.0)은 22일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Figma Context MCP, Claude Real Video, Figma MCP Server
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:↓ 주간 다운로드i★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Persistent memory for visual intelligence. Analyze once. Remember forever.
Click an install button to add the MCP server in one step. You'll be prompted to fill in
PHOTOTOLOGY_API_KEYafter install. For other editors, see the Setup section below.
MCP server for Phototology, the visual-intelligence registry. Any MCP-speaking agent (Claude Code, Cursor, VS Code Copilot, Claude Desktop) calls Phototology as a tool. Photos are analyzed once, keyed by perceptual hash, then returned free forever to any future caller on the same account.
npx @phototology/mcp
The interactive wizard asks for your API key and writes the config for whichever editor you pick.
Without: every agent that needs to understand a photo pays a vision model from scratch. Same image, same question, full price every time.
With: the first call bills 1 credit per lens. Every subsequent call on that photo returns the cached lens result for free. The registry compounds with use.
Reach for it when:
Look elsewhere when:
| Capability | Stateless vision wrapper | Phototology |
|---|---|---|
| Repeat calls on the same photo | Pays the vision model again every time | Cached free forever, keyed by perceptual hash |
| Memory across calls | None | Persistent registry (sha256 + pHash) |
| Output | Freeform text | Structured JSON, one field set per lens |
| Cost to the agent | Opaque | Per-tool, per-lens credit cost disclosed |
| Near-duplicate detection | None | Exact (sha256) and perceptual (pHash) matching |
The persistent registry, the perceptual-hash dedup, and the free re-reads are the difference.
Stated plainly, so an agent can route correctly:
people lens reports physical observations and a head count, never a name or a face match.purchase_credits returns a wallet deep-link. Stripe checkout needs a browser.enrich_photo writes EXIF, IPTC, and XMP. formats: ["c2pa"] is rejected.| Tool | What it does | Cost |
|---|---|---|
analyze_photo | Run AI vision against ONE image and return structured facts per lens. Accepts imageUrl, imageBase64, or imagePath. | 1 credit per lens. Re-running cached lenses on the same photo: 0. |
analyze_batch | Analyze 1 to 200 photos in a single call. Internally lookup-first per URL (20 in flight), then analyzes cache-miss photos with bounded concurrency (5 in flight, matching the API per-user cap). For thousands, loop in slices of 200. Accepts imageUrls, imagesBase64, or imagePaths (with glob support). Returns { summary, results }; pass dryRun: true to see what a batch would cost without spending. | 1 credit per lens per non-cached photo. Cache hits free. A dry run is free. |
lookup_photo | Check if a single photo has already been analyzed; return all cached lens results. Accepts sha256, pHash, imageUrl, imageBase64, or imagePath (cascade). | Free. |
enrich_photo | Write cached lens output into a photo's EXIF/IPTC/XMP metadata so the structured intelligence travels with the file. Accepts imageUrl, imageBase64, or imagePath plus an optional outputPath that saves the enriched copy as a new file (the original is never written; an existing file is refused unless overwrite: true). Returns a small JPEG preview of the enriched photo. A photo carrying a valid content credential is left untouched: the analysis is saved beside it as .xmp (sidecarSavedTo), or returned as sidecar.xmp, with mode: 'sidecar', reason, credentials and a plain-words note. Pass breakCredentials: true to write a copy anyway. | 5 credits per call. |
list_lenses | Enumerate available lenses and stacks with descriptions and output fields. | Free. |
get_credits | Read the account credit balance: total spendable credits and its dollar value usd, plus reserved in-flight holds when there are any. | Free. |
purchase_credits | Return a wallet deep-link the user can open to buy more credits. | Free. |
How they chain. Start free: lookup_photo for a single image, or get_credits before a batch. Analyze only the cache misses with analyze_photo (one image) or analyze_batch (2 or more). Bake the results into the file with enrich_photo. list_lenses and purchase_credits are free helpers you can call any time.
| URI | What it serves |
|---|---|
phototology://lenses | The lens and stack catalog, the same data list_lenses returns. |
phototology://photo/{sha256} | One registry record: its hashes, the lenses already run on it, and their receipts. Free to read. A photo not in the registry reads as PHOTO_NOT_IN_REGISTRY. |
analyze_photo, analyze_batch and lookup_photo link each photo they name to its phototology://photo/{sha256} resource.
Analyze and lookup results carry receipts: signed records of which lenses ran on which photo, with which model, and when. Results also carry credentials when the API read the photo's content credential. To check a receipt, use verifyReceiptSignature (offline) or client.verifyReceipt (server-side, free) from @phototology/sdk.
Who gets receipts today. Only pt_test_ keys receive receipts right now. Those are signed by the published sandbox key, which never vouches for live data: they check your integration, not a photo. Production has no published live signing key yet, so until it is, a pt_live_ key receives receipts: [] with the warning RECEIPT_UNAVAILABLE on a fresh analyze, and with RECEIPT_NONE_STORED on a result served from the registry (no live receipt has been stored yet). The analysis and its billing are unaffected.
When receipts is empty on an analyze result, warnings names why (a fresh analyze whose lenses produced no output issues neither a receipt nor a warning):
| Code | Meaning |
|---|---|
RECEIPT_UNAVAILABLE | No receipt could be signed for this call: no live signing key is configured and published, or signing failed or timed out. The analysis itself is complete. |
RECEIPT_NONE_STORED | The result came from the registry, and no stored receipt covers the lenses asked for (the analysis predates receipts or was issued without one). |
RECEIPT_NOT_ISSUED_MULTI_IMAGE | The call sent more than one image; receipts are issued for single-image calls only. |
RECEIPT_NOT_ISSUED_BESPOKE | The call used a custom extraction schema; those calls get no receipt. |
RECEIPT_NOT_ISSUED_NO_ACCOUNT | The key is not linked to an account, so there is no registry record for a receipt to name. |
analyze_photo results and analyzed analyze_batch rows carry these warnings. lookup_photo and batch rows served from the registry carry the photo's stored receipts and no receipt warning: an empty list there means no stored receipt covers that photo.
Credentials on `enrich_photo`. The credentials block on an enrich result describes the input photo, as read before the write. With breakCredentials: true, the written copy no longer carries a valid content credential, even when credentials reports one on the input.
| Pack | Credits | Price |
|---|---|---|
| Starter | 1,000 | $10 |
| Pro | 10,000 | $100 |
| Business | 100,000 | $1,000 |
First purchase doubles. Your first pack ever credits 2x. A Starter $10 buys 2,000 credits the first time. No subscriptions; pay-as-you-go via packs only.
All three image-accepting tools (analyze_photo, analyze_batch, lookup_photo) accept three input modes. Pick whichever fits how the photo arrived:
| Input field | When to use |
|---|---|
imageUrl | The photo is at a publicly-fetchable URL. |
imagePath | The photo is on the user's local disk as seen by the machine running the MCP server (typically the user's laptop). Pass an absolute path (/Users/...) or ~/-prefixed (~/photos/...). The MCP reads the file, validates it, and forwards the bytes. Not for agent-sandbox paths (e.g. Claude's /mnt/user-data/uploads/...): that sandbox is invisible to a locally-installed MCP. For agent-sandboxed uploads, use imageBase64 (small images) or imageUrl (hostable images of any size). |
imageBase64 | You already have the image bytes in memory as a base64 string. Practical envelope: ~50-150KB JPEG / ~70-200K base64 chars. Above that, LLM-driven clients run out of output-token budget before the MCP receives the full payload (a 3.4MB JPEG produces ~4.57M chars). For larger images, prefer imageUrl or imagePath. |
For batch: use imagePaths: string[] for local paths (supports globs like ~/vacation/*.jpg), imagesBase64: string[] for base64 arrays, and combine freely with imageUrls.
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