Figma Context MCP
Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
作者提供的说明(英文原文)
Convert DOCX/XLSX to Markdown — native chart-data extraction, optional VLM for figures
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"refigure": {
"command": "uvx",
"args": [
"refigure"
]
}
}
}在终端中运行这条命令
claude mcp add refigure -- uvx refigure粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"refigure": {
"command": "uvx",
"args": [
"refigure"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"refigure": {
"type": "stdio",
"command": "uvx",
"args": [
"refigure"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 HelgDemidov 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.3.5)于3个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Converters where figures survive.
DOCX/XLSX → Markdown that keeps charts and infographics machine-readable instead of losing them to OCR or a vision model: native OOXML chart data (numCache/strCache) recovers exact numbers with zero GPU calls, zero VLM calls, zero lost precision — by default, not as a fallback.
That default path is also why the base install (pip install "refigure[docx,xlsx]") is ~500x lighter than PyTorch-based alternatives (5.6MB vs. multi-GB) — the core conversion needs no ML model at all. That number is about the core architecture, not every distribution format: the Docker image trades it back deliberately, bundling VLM providers + LibreOffice for a turnkey composite-figure path (see Docker below).
VLM interpretation itself is there for the rare figure with no native data at all (a dashboard screenshot) — never required just to get real numbers out of a chart, on any distribution format.
Ships as a library, CLI, MCP server, and a one-click Claude Desktop bundle — every surface returns the same native-fidelity output, not a degraded summary for agents.
numCache/strCache directly; no rasterize/OCR/VLM step for charts, real numbers every time.[vlm] extra, --vlm/Config(use_vlm=True)) — cloud description + a real rendered mermaid diagram (26 supported diagram types — flowcharts, pie/xy charts, sequence/state/ER diagrams, Gantt/timeline/sankey/treemap and more, see Status below) on top of the zero-loss floor, for figures with no native chart data at all (e.g. a dashboard screenshot). Provider-agnostic — OpenRouter by default, or direct OpenAI/Ollama/vLLM/LM Studio/Anthropic via --vlm-provider ([vlm-direct] extra). --strict upgrades one specific failure (the system soffice/LibreOffice binary missing) from a graceful skip to a hard error; every other VLM failure still degrades.ConversionResult (markdown + warnings + chart/group counts + vlm_used), not a bare string.refigure console command, stdin/stdout-first, native batch mode, typed exit codes (see below).refigure-mcp console command ([mcp] extra), stdio or Streamable HTTP, tools/resources/prompts, batch conversion with per-file isolation (see below).ghcr.io/helgdemidov/refigure, both console commands on PATH, soffice/LibreOffice baked in — the VLM composite-figure path works turnkey, no manual LibreOffice install. Multi-arch — linux/amd64 + linux/arm64, native Apple Silicon (see below).docx+xlsx only, see below).Optional VLM interpretation — for a figure with no native chart data at all (a screenshot, not an OOXML chart part) AND no matching mermaid construct either (a dense radial sunburst — nothing in the 4 original mermaid types could represent it), --vlm both recovers the real content and produces a genuinely renderable diagram, not just recovered text:
Native chart-data extraction — real OOXML numCache, not a screenshot, not OCR:
Same extraction, from DOCX — Word embeds native charts too, not just Excel; refigure reads the same cached OOXML data either way:
Composite figures — positioned, zero-loss, even when the figure itself can't be rendered (no incumbent does this — see Docling issue #1287, open >1 year):
pip install "refigure[docx,xlsx]"
refigure report.docx # markdown to stdout
from refigure.docx import convert
result = convert("report.docx") print(result.markdown) print(f"{result.charts_found} charts, {result.groups_found} composite figures")
Or without a permanent install, via uv/uvx:
uvx --from "refigure[docx,xlsx]" refigure report.docx
Optional VLM interpretation, for a composite figure the chart engine can't reconstruct on its own (see Features above):
pip install "refigure[docx,vlm]"
export OPENROUTER_API_KEY=... # or --vlm-api-key-file/--vlm-provider refigure report.docx --vlm # needs the system soffice/LibreOffice binary too
One converter, four ways to run it — pick whichever fits your pipeline. Click a heading to expand it.
CLI — a console command, stdin/stdout-first, native batch mode
refigure installs a console command — a thin wrapper over the same convert() used programmatically, no separate logic:
refigure report.docx # markdown to stdout
refigure report.docx -o report.md # markdown to a file cat report.docx | refigure --format docx # stdin, format hint required refigure reports/ -o out/ # batch: directory, walked recursively refigure a.docx b.xlsx -o out/ # batch: 2+ explicit sources
Batch mode (2+ sources, or a single directory) requires -o DIR, keeps going past a failed source by default (--fail-fast aborts on the first one instead), and always prints a summary (N/M converted, K failed) to stderr. --json emits the full result — markdown plus chart/group counts and warnings — instead of plain markdown. -v/-q control verbosity; --strict is forwarded to the same Config.strict the Python API uses.
Exit codes:
| Code | Meaning |
|---|---|
| 0 | success |
| 1 | batch mode: 1+ sources failed (keep-going default) |
| 2 | usage error (bad arguments/flags) |
| 3 | input isn't a valid document of its format |
| 4 | input isn't a valid/safe archive |
| 5 | the format's extra ([docx]/[xlsx]) isn't installed |
| 6 | unexpected internal error |
MCP server — for agents/IDEs that speak the protocol directly
refigure-mcp — the same converters as an MCP server, for agents/IDEs that speak the protocol directly instead of shelling out to a CLI or importing the library. Listed on the official MCP Registry as io.github.HelgDemidov/refigure:
pip install "refigure[mcp,docx,xlsx]"
refigure-mcp # stdio — the MCP client launches it
{
"mcpServers": { "refigure": { "command": "refigure-mcp" } } }
Or point the client at uvx instead, with no permanent install at all:
{
"mcpServers": { "refigure": { "command": "uvx", "args": ["--from", "refigure[mcp,docx,xlsx,vlm-direct]", "refigure-mcp"] } } }
refigure[full] is a shortcut for refigure[mcp,docx,xlsx,vlm-direct] — every tool, both formats, every VLM provider, one extras string.
Three tools — convert_docx, convert_xlsx, and convert_batch (multiple files in one call: one bad file reports its own error without aborting the rest) — each registered only if its format extra is actually installed. use_vlm/--vlm-provider and friends work the same as the CLI. A result too large to inline is stored and handed back as a refigure://conversion/{id} resource instead of inflating the tool response. Two prompts (ingest_for_rag, explain_conversion_warnings) help a client pick the right tool/VLM settings for the job.
Streamable HTTP is opt-in, for a shared/remote deployment — bearer-token auth is required, not optional:
echo "sk-... = alice" > tokens.txt
refigure-mcp --transport http --mcp-auth-token-file tokens.txt
Per-caller rate-limiting (protects the operator's own spend from a leaked/runaway token) applies automatically over HTTP, together with a fairness soft-cap once 2+ callers are configured; refigure-mcp --help covers every tuning flag (concurrency, timeouts, resource-store limits, batch size, VLM ceiling).
Docker — one image, CLI and MCP server both on PATH, soffice baked in
One image, both surfaces — refigure and refigure-mcp are already on PATH, no separate CLI/MCP builds to choose between. The one thing this format buys over pip/uvx that neither can: the system soffice/ LibreOffice binary the VLM composite-figure path needs is baked in, not a manual install. Multi-arch manifest (linux/amd64 + linux/arm64) — docker pull resolves the right layer automatically, including on Apple Silicon.
docker pull ghcr.io/helgdemidov/refigure:latest
Pin an exact version instead of :latest for reproducibility — e.g. :0.3.4 — see the package page for
只显示了开头部分;其余内容请在仓库中阅读