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)
TokenLab MCP server: generate images and video, call models. 32 default and 89 full-profile tools.
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.
TOKENLAB_API_BASE, TOKENLAB_API_KEY, TOKENLAB_REQUEST_TIMEOUT_MS, TOKENLAB_MCP_TOOL_PROFILE, TOKENLAB_MCP_SCHEMA_MODE, TOKENLAB_MCP_MAX_FILE_BYTES, TOKENLAB_MCP_INLINE_BYTES, TOKENLAB_ARTIFACT_DIR, TOKENLAB_MANAGEMENT_TOKEN비밀로 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": {
"tokenlab": {
"command": "npx",
"args": [
"-y",
"@tokenlabai/mcp-server"
],
"env": {
"TOKENLAB_API_BASE": "your-value",
"TOKENLAB_API_KEY": "your-value",
"TOKENLAB_REQUEST_TIMEOUT_MS": "your-value",
"TOKENLAB_MCP_TOOL_PROFILE": "your-value",
"TOKENLAB_MCP_SCHEMA_MODE": "your-value",
"TOKENLAB_MCP_MAX_FILE_BYTES": "your-value",
"TOKENLAB_MCP_INLINE_BYTES": "your-value",
"TOKENLAB_ARTIFACT_DIR": "your-value",
"TOKENLAB_MANAGEMENT_TOKEN": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add tokenlab --env TOKENLAB_API_BASE=your-value --env TOKENLAB_API_KEY=your-value --env TOKENLAB_REQUEST_TIMEOUT_MS=your-value --env TOKENLAB_MCP_TOOL_PROFILE=your-value --env TOKENLAB_MCP_SCHEMA_MODE=your-value --env TOKENLAB_MCP_MAX_FILE_BYTES=your-value --env TOKENLAB_MCP_INLINE_BYTES=your-value --env TOKENLAB_ARTIFACT_DIR=your-value --env TOKENLAB_MANAGEMENT_TOKEN=your-value -- npx -y @tokenlabai/mcp-server실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"tokenlab": {
"command": "npx",
"args": [
"-y",
"@tokenlabai/mcp-server"
],
"env": {
"TOKENLAB_API_BASE": "your-value",
"TOKENLAB_API_KEY": "your-value",
"TOKENLAB_REQUEST_TIMEOUT_MS": "your-value",
"TOKENLAB_MCP_TOOL_PROFILE": "your-value",
"TOKENLAB_MCP_SCHEMA_MODE": "your-value",
"TOKENLAB_MCP_MAX_FILE_BYTES": "your-value",
"TOKENLAB_MCP_INLINE_BYTES": "your-value",
"TOKENLAB_ARTIFACT_DIR": "your-value",
"TOKENLAB_MANAGEMENT_TOKEN": "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": {
"tokenlab": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@tokenlabai/mcp-server"
],
"env": {
"TOKENLAB_API_BASE": "your-value",
"TOKENLAB_API_KEY": "your-value",
"TOKENLAB_REQUEST_TIMEOUT_MS": "your-value",
"TOKENLAB_MCP_TOOL_PROFILE": "your-value",
"TOKENLAB_MCP_SCHEMA_MODE": "your-value",
"TOKENLAB_MCP_MAX_FILE_BYTES": "your-value",
"TOKENLAB_MCP_INLINE_BYTES": "your-value",
"TOKENLAB_ARTIFACT_DIR": "your-value",
"TOKENLAB_MANAGEMENT_TOKEN": "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.
TOKENLAB_API_BASE envTOKENLAB_REQUEST_TIMEOUT_MS envTOKENLAB_MCP_TOOL_PROFILE envTOKENLAB_MCP_SCHEMA_MODE envTOKENLAB_MCP_MAX_FILE_BYTES envTOKENLAB_MCP_INLINE_BYTES envTOKENLAB_ARTIFACT_DIR 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 6일 점검
레지스트리 이름이 GitHub 계정 hedging8563 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
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: TOKENLAB_API_KEY, TOKENLAB_MANAGEMENT_TOKEN
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(0.6.44)은 어제 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:★ 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.
OpenAPI-generated Model Context Protocol server for TokenLab public model discovery, pricing, native LLM endpoints, typed decisions, multimodal generation, async tasks, files, embeddings, rerank, translation, resources, prompts, and the broader developer API.
It exposes public catalog tools for agents that need to choose models, inspect supported request formats, or compare pricing before calling TokenLab APIs. Credentialed tools cover text inference, image generation and editing, video, music, 3D, async task polling, embeddings, rerank, typed decisions, and text translation.
The checked-in generated/tools.json manifest is generated from TokenLab's public OpenAPI document plus the small MCP-only overlay in contract/mcp-overlay.json. Version 0.6.44 generates 87 endpoint tools; with the two MCP-only composite discovery tools, the full profile returns 89 tools from tools/list.
| Profile | Endpoint tools | Total registered tools | Model-facing schema | Coverage |
|---|---|---|---|---|
catalog | 4 | 6 | Exact | Public model discovery and pricing only; no API key required |
core (default) | 30 | 32 | Portable | Catalog and pricing; Chat Completions, Responses, Anthropic Messages, Gemini generateContent; System One typed decisions; images, video, music, 3D, speech and transcription; async tasks; files; embeddings, rerank, and translation |
full | 87 | 89 | Portable | Every allowlisted developer API operation in the checked-in OpenAPI snapshot, including core plus response lifecycle, batches, worlds, and native model discovery |
The total registered count is the number returned by tools/list. All profiles include compare_models and get_api_overview, producing totals of 6, 32, and 89 tools. Realtime and streaming-only operations are excluded because stdio MCP tool calls return one final result. API operations that accept stream fix it internally to false without exposing a boolean const to provider adapters, and the Gemini query-string API key is intentionally hidden from tool arguments. create_gemini_content exposes the camelCase Gemini fields (systemInstruction, generationConfig, safetySettings, toolConfig, cachedContent) and hides their snake_case proto spellings, which the API accepts as aliases.
The portable projection keeps every top-level argument but bounds deeply nested model-facing shapes. The server still validates calls against the complete generated OpenAPI schema before issuing an API request. Compatibility budgets keep core at no more than 60 KB and depth 8, and full at no more than 115 KB and depth 8 for the complete tools/list response. Tests also run the full profile through the Google AI SDK version used by the observed OpenCode/Gemini failure and keep its function declarations within 90 KB.
Set TOKENLAB_MCP_TOOL_PROFILE=catalog for the smallest public-only tool list or TOKENLAB_MCP_TOOL_PROFILE=full for the broad developer API. Set TOKENLAB_MCP_SCHEMA_MODE=exact only when a client needs the complete nested JSON Schema and can accept its larger/deeper tool payload. Use strict for providers that require every property to be listed in required and every object to set additionalProperties: false; complex top-level arguments are represented as JSON-encoded strings and decoded before canonical validation. Canonical tool names, descriptions, input JSON Schemas, HTTP bindings, content types, auth requirements, and task behavior can be inspected in generated/tools.json.
The smaller generated/public-contract.json is the machine-readable projection used by TokenLab's website and other public consumers. It contains package identity, profile counts, core tool layers, resources, prompts, and source hashes without copying all endpoint schemas.
structuredContent while retaining serialized text for older clients.exact mode is byte-shape equivalent to the generated canonical schema.choose_tokenlab_model and build_tokenlab_request prompts guide agents to use live model truth and preserve native endpoint shapes.npm install
npm start
Install from npm:
npx -y @tokenlabai/mcp-server
Agent-assisted installers can follow llms-install.md for a credential-safe setup and verification flow.
Run in Docker:
docker build -t tokenlab-mcp-server .
docker run --rm -i tokenlab-mcp-server
Add -e TOKENLAB_API_KEY when using credentialed API tools. Public catalog tools do not require a key.
Claude Desktop style config:
{
"mcpServers": { "tokenlab-model-catalog": { "command": "npx", "args": ["-y", "@tokenlabai/mcp-server"], "env": { "TOKENLAB_API_BASE": "https://api.tokenlab.sh" } } } }
No TokenLab API key is required for public catalog and pricing operations. Set TOKENLAB_API_KEY when credentialed tools should call TokenLab APIs. Generated tools preserve the OpenAPI request shape for OpenAI-compatible and native endpoints instead of flattening them into a shared prompt format.
Multipart operations accept local file paths. Small image and audio responses are returned as native MCP content; larger or other binary responses are written to TOKENLAB_ARTIFACT_DIR and returned as a path with MIME type and byte count.
Video, music, and 3D creation tools always return an async task. Image generation and editing may return a completed result or an async task depending on the selected model and request.
Media tools preserve the complete TokenLab API response under response and add a normalized delivery summary:
{
"delivery": { "mode": "async", "task_id": "ldtask_...", "status": "pending", "poll_url": "/v1/tasks/ldtask_...", "terminal": false, "next_tool": "get_task_status" }, "response": {} }
Use delivery.mode instead of assuming all image requests are synchronous. For async tasks, call get_task_status with { "id": delivery.task_id } until delivery.terminal is true. Completion is determined from status, not from an optional progress field.
TOKENLAB_API_BASE: optional, defaults to https://api.tokenlab.shTOKENLAB_API_KEY: optional; required for text inference, multimodal generation, async task, embedding, rerank, decision, and translation toolsTOKENLAB_MCP_TOOL_PROFILE: optional, catalog, core (default), or fullTOKENLAB_MCP_SCHEMA_MODE: optional, portable, exact, or strict; defaults to the selected profile's tested modeTOKENLAB_REQUEST_TIMEOUT_MS: optional request timeout in milliseconds, defaults to 120000TOKENLAB_MCP_MAX_FILE_BYTES: optional maximum local upload size per file, defaults to 104857600 (100 MiB)TOKENLAB_MCP_INLINE_BYTES: optional maximum binary/JSON response size returned inline, defaults to 2097152 (2 MiB)TOKENLAB_ARTIFACT_DIR: optional output directory for non-inline response artifacts, defaults to the OS temp directory under tokenlab-mcpFor Chat Completions image inputs, prefer byte-accurate data URLs such as data:image/png;base64,.... If an MCP caller labels a recognized PNG, JPEG, WebP, or GIF payload as application/octet-stream, the server corrects that generic MIME before forwarding. An unrecognized generic binary payload is rejected locally with a precise input error.
The public OpenAPI document is the API contract source. The overlay contains only MCP-specific choices: profile exposure, stable tool aliases, secret omission, non-streaming constraints, content-type variants, async task semantics, and the compact public projection consumed by the website and docs gates.
npm run contract:source-check # compare the snapshot with the live canonical OpenAPI (read-only)
npm run contract:check # check generated output against the checked-in snapshot (offline) npm run contract:sync # fetch OpenAPI and regenerate; refuses dirty outputs or a stale branch npm test # compile profiles and test exact/portable/strict schemas, provider conversion, routing, tasks, files, and binary output
Always run git pull --ff-only before a manual contract sync. contract:check proves internal consistency only; contract:source-check proves freshness against the canonical source. The scheduled Sync TokenLab OpenAPI contract workflow runs the full write sequence and commits only the verified OpenAPI snapshot and generated manifest to main. A failed fetch, stale local branch, dirty generated output, generation error, schema compilation error, or test leaves the tracked contract unchanged.
This repository includes server.json for the official MCP Registry.
Release metadata:
@tokenlabai/mcp-server@0.6.44io.github.hedging8563/tokenlabpackage.json.mcpName: io.github.hedging8563/tokenlabFor a new release:
package.json, package-lock.json, and server.json.v0.6.0.The same workflow can be run manually from main to republish only the current MCP Registry metadata. No npm or MCP Registry token is stored in GitHub.
Use the catalog profile when no credentialed tools are needed. Keep TOKENLAB_API_KEY in the local MCP client's secret environment, enable human confirmation for billable and destructive calls, and review tool annotations before granting persistent approval. Do not send a TokenLab API key to an untrusted hosted MCP server.
Each API request sends X-TokenLab-Client-Source: mcp- (for example mcp-create_image) so TokenLab can count which tools are used. Reading the tokenlab://api/overview resource sends mcp-resource-api_overview. The header holds only the lowercased tool name, cut to 64 characters, and no tool arguments.
Use the full profile to configure workspace webhooks with list_webhooks, create_webhook, get_webhook, update_webhook, delete_webhook, rotate_webhook_secret, test_webhook and list_webhook_deliveries.
Set `TOKENLAB_MANAGEMENT
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요