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)
Keeps AI coding agents' plan and design markdown managed and versioned, for Claude Code and Codex.
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.
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": {
"flanner": {
"command": "uvx",
"args": [
"flanner"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add flanner -- uvx flanner프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"flanner": {
"command": "uvx",
"args": [
"flanner"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"flanner": {
"type": "stdio",
"command": "uvx",
"args": [
"flanner"
]
}
}
}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.
이 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 7일 점검
게시자(jaysonmulwa)가 저장소 소유자(elandlabs)가 아니에요. 다른 사람이 등록했을 수 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 0개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
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(0.16.0)은 30일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 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.
A plan-file manager for AI coding agents, wired into Claude Code and other assistants over MCP (Model Context Protocol).
AI agents write markdown constantly: design docs, migration plans, architecture notes. It piles up fast, scattered across your repo, quietly going stale, and easy to commit by accident. Flanner gives those files one home, versions them automatically as the agent revises, and keeps them out of git until you decide otherwise, with a browsable reading view and an audit trail on top.
Those plan files pile up in two directions at once: scattered across your projects locally, and scattered across open issues in your project-management tool. Flanner is the choke point for both, keeping you organized on disk and linked to the issue each plan belongs to.
Flanner is local-first, and stays that way when a team uses it. Plans sync directly between your machines over an encrypted connection — there is no server holding them, and no upload step. The hosted side (Flanner Mesh) issues identities and decides who may read what; it never sees plan contents and could not read them if it wanted to. Next after that: clean links out to the tools teams already work in, from product trackers and chat to second brains like Notion.
.plans/ and are kept out of commits automatically.flanner init wires CLAUDE.md, AGENTS.md, and a guard hook so agents save plans through flanner instead of scattering raw markdown.flanner skills scan reads them all and flanner skills doctor says which copy is in effect and what is wrong with the rest. A read: nothing in a package is executed.flanner curb map checks every Claude Code and Codex launch for readable credentials and for the channels that could carry them away, and rates it High, Medium or Low with the rule behind it. Its output never names a credential or a location, whoever runs it; flanner curb show puts those in a window on your screen. flanner curb sweep finds secrets agents left behind in transcripts, sessions, instruction files, skills, MCP configs, shell history and .env files, and says which were sent to a model provider. A read: nothing is changed, and nothing leaves the machine unless you ask a secret's own issuer to check it. With Flanner Mesh, flanner curb policy takes one agent policy your organization signs, applies the changes that only tighten once you enrol, and holds anything else for your approval; flanner curb fleet shows admins each device's policy state and counts, from minimised reports each device signs. flanner curb ci, also a GitHub Action at actions/curb-ci, judges agent steps in CI workflows the same way and writes SARIF; flanner curb app finds an application's own LLM calls; and the agent-blast-radius skill lets an agent run the redacted commands itself. flanner curb attribution gives each agent a signing key of its own, kept in your OS credential store, so its commits are labelled; flanner curb verify says which agent key signed each commit, checked against your organization's signed key registry. A signature shows which key signed, not who wrote the code.flanner messages send previews before it sends, and flanner tells an agent to show a teammate's message and never act on it.LINEAR_API_KEY, flanner verifies the issue and shows its live state, in the CLI and the dashboard. Built but not supported yet: set FLANNER_INTEGRATIONS=1 to turn them on.uv tool install flanner # or: pipx install flanner, or: pip install flanner
cd your-project # a git repo where plans should live flanner init # sets up the database, MCP registration, and a project
Rather not install Python? The desktop app for Windows, macOS and Linux is the same flanner with nothing to install first: the web UI in a window, the command line, and the MCP server Claude and Codex use, with background sync and updates. Download it from flanner.io/download; desktop/README.md covers building and releasing it.
uv tool install gives flanner an environment of its own and puts both flanner and flanner-mcp on your PATH. Both matter: your agent spawns flanner-mcp by name, so a project virtualenv can hide it from an agent started outside that environment. pip install flanner works, with that caveat.
Then ask your agent to work with plans:
"Create an architecture plan for the auth service"
"Show me the history of the architecture plan"
And open the dashboard to browse them:
flanner web --open-browser # http://localhost:8080
flanner init is safe to re-run. It detects your git root, creates .plans/, updates .gitignore, and installs the agent integration.
Three agents, three different files, and init handles all of it — there is no second command to remember. It writes a project-scoped .mcp.json that Claude Code reads, registers the server at user scope and in Claude Desktop's config, and adds managed blocks to CLAUDE.md and AGENTS.md. Codex reads AGENTS.md and registers MCP servers in ~/.codex/config.toml; when Codex is installed, init adds flanner's entry there, and prints the two lines to paste only when it cannot edit the file safely. flanner status shows a row per agent, each checked where that agent actually looks, and flanner setup re-runs the registration on its own if one of them needs repairing later.
--setup picks which of them you want, and repeats: flanner init --setup codex registers Codex and nothing else. --skip-claude registers with no agent, though the repository's own files are still written.
Adopting a repository somebody else set up? flanner init --sync also imports the plan files already committed in it, so flanner list shows them straight away.
flanner init [--project-root PATH] [--plan-dir DIR] # set up a project
flanner status # projects, plan files, db path flanner list [--project NAME] [--output json] # list projects or a project's plans flanner sync [--project NAME] [--dry-run] # import existing .plans/ files flanner config NAME [--plan-dir DIR] [...] # change project settings flanner web [--port 8080] [--host 127.0.0.1] [--open-browser] flanner start [--port 8765] / flanner stop # MCP server in the background, over http flanner peer start / flanner peer stop # serve plans to teammates in the background flanner mem remember "..." --category fact # durable context for later sessions flanner mem recall "..." # search what earlier sessions knew flanner mem list / show ID / supersede ID --with "..." # browse, read, and correct flanner mem mode [off|explicit|suggest|auto_safe] # how much this project captures flanner mem share ID / flanner mem withdraw ID # give one to the team, or ask them to stop flanner skills scan / flanner skills doctor # what your agents load, and what is wrong flanner skills list [--all] / inspect NAME # browse them, or read every copy of one flanner skills observe enable / flanner skills report # record which get used; init asks flanner skills adopt NAME / install HASH / rollback ID # keep a copy, install it, put it back flanner skills share HASH / transfers / import ID # send one to the team; receiving is not installing flanner curb map [--agent A] [-- LAUNCH ...] # what each agent launch can reach flanner curb show # names and locations, in a window flanner curb inventory # agents, MCP servers, hooks, jobs flanner curb sweep [--validate] # secrets agents left behind (needs flanner[sweep]) flanner curb forget # delete Curb's reports and digest key flanner curb fix [--dry-run|--undo] # close what agents reach, after your OS says yes flanner curb test # prove each block with decoys (costs tokens) flanner curb decoys [--renew|--remove] # the decoys the tester planted flanner curb scrub FILE [--dry-run] # replace rotated secrets in a file (no undo) flanner curb log [--enable|--disable|--verify] # each tool call's metadata, hash-chained and signed flanner curb observed # what each agent has been seen using flanner curb policy [--check-in|--enrol|--approve] # your organization's signed agent policy flanner curb fleet # every device, checked by its own signature (admins) flanner curb ci [PATH] [--sarif F] [--fail-on L] # agent steps in CI workflows, as SARIF flanner curb app [PATH] [--sarif F] # LLM calls in an app's Python code flanner curb attribution [--setup|--rotate] # sign each agent's commits with its own key flanner curb verify [REV|RANGE] # which agent key signed each commit flanner register [--force] / flanner unregister # MCP registration with Claude Desktop flanner claude-info # integration status
Most MCP clients spawn their own copy of the server over stdio and need neither start nor stop. They are for a client that only speaks http, two editors sharing one server, or working with flanner on its own. The server binds 127.0.0.1 and no option widens that: every tool acts with the full authority of whoever started it, and nothing authenticates a caller.
Plan file format
Every managed plan carries YAML frontmatter, generated by the tools and never hand-written:
---
mcp_plan_file: true project_id: 3d816ecd-489a-4fa0-abe2-15ec93f60d5a plan_file_id: 59c34f9c-8471-47fc-97f2-8dcfefa15434 plan_name: architecture version: 2 created_by: claude
Your plan content here...
</d
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요