原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

anymd

作者:SylphxAI版本 8.5.11i★ –i在你的电脑上运行
注册名称io.github.SylphxAI/anymd

安装

概览

作者提供的说明(英文原文)

Any file → clean Markdown for AI agents: PDF, Office, EPUB, HTML, images, audio/video. Local MCP.

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 设计、图片与音频

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

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": {
    "anymd": {
      "command": "npx",
      "args": [
        "-y",
        "@sylphx/anymd"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add anymd -- npx -y @sylphx/anymd

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "anymd": {
      "command": "npx",
      "args": [
        "-y",
        "@sylphx/anymd"
      ]
    }
  }
}

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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "anymd": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@sylphx/anymd"
      ]
    }
  }
}

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。

它能做什么

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。

基础检查i

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8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 SylphxAI 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    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“设计、图片与音频”中的其他服务器比较i

  • 星标数多于此分类中其他 2767 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 2767 个服务器的 99% 更新
  • 基础检查结果等于或优于此分类中其他 2767 个服务器的 100%

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。

常见问题

anymd 安全吗?

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。

它需要哪些密钥或代码?

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。

它在我的电脑上运行还是在云端?

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)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(8.5.11)于今天进入注册中心。

哪些应用可以使用它?

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 Node.js。

有什么可以替代它?

同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server

替代选择

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

Figma Context MCP

作者:GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

在你的电脑上运行★ 16,165MIT5个月前更新
安装

Claude Real Video

作者:HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

在你的电脑上运行★ 2,284MIT2个月前更新
安装

Figma MCP Server

作者:figma

The Figma MCP server brings Figma design context directly into your AI workflow.

在云端运行★ 2,15213个月前更新
安装

作者的 README

打开 README

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。

anymd

PDF, Word, PowerPoint, Excel, EPUB, HTML and web pages, images (OCR), audio and video (metadata, subtitles, transcripts). A fast Rust MCP server and CLI that runs on your machine. No API key.

anymd is the free, local mode of the same crates that run Sylphx AI parse, the hosted document-parse service: the same crates, hosted and metered per page for callers that want no local runtime.

Install · Benchmarks · Tools · CLI · Formats · Docs · Pro

Formerly pdf-reader-mcp. Migrating from pdf-reader-mcp

A real, unedited terminal recording (asciinema + agg, script ). The last command is Claude Code answering from the PDF through the anymd MCP server.

npx -y @sylphx/anymd setup              # add anymd to every MCP client on this machine

npx -y @sylphx/anymd report.pdf > report.md # or convert from the shell

Why anymd

  • Fast. Native Rust converts in parallel, page by page. On the 38 benchmark documents, anymd takes 11.5 s in total; docling 2,432.4 s (212×), markitdown 75.6 s (7×); marker converted 30 of them in 7,104.5 s, against anymd's 4.64 s on the same 30 (1,532×).
  • Accurate. A layout engine rebuilds words from glyph gaps, puts two-column papers in reading order, and recovers tables, including borderless ones. The text stays exactly as printed, with no glued words and no scrambled columns.
  • Lean on tokens. Pages come back as Markdown with citation anchors, a small front-matter header, and compact tables. A token budget and a cursor keep large documents within your agent's context.
  • Every format, one call. One tool reads every format listed below. It also accepts web URLs and whole directories, and search looks across all of them.
  • Local and private. Nothing is uploaded. OCR uses installed local doc-VLM models or tesseract; transcripts use ffmpeg and bundled Qwen3-ASR. OCR setup and ASR model downloads require explicit opt-in.

Install

Add anymd to every MCP client on your machine (Claude Code, Codex, Cursor, VS Code, Claude Desktop, Windsurf, Gemini CLI) with one command:

npx -y @sylphx/anymd setup     # --dry-run to preview, --remove to undo

Or add it by hand: every MCP client runs the same command, npx -y @sylphx/anymd. Node 18+ is the only requirement; npm installs the native binary for your platform.

Claude Code

claude mcp add anymd -- npx -y @sylphx/anymd

Or as a plugin, with the anymd skill: /plugin marketplace add SylphxAI/anymd, then /plugin install anymd@anymd.

Codex

codex mcp add anymd -- npx -y @sylphx/anymd

or in ~/.codex/config.toml:

[mcp_servers.anymd]

command = "npx" args = ["-y", "@sylphx/anymd"]

</details>

Cursor

or in .cursor/mcp.json:

{ "mcpServers": { "anymd": { "command": "npx", "args": ["-y", "@sylphx/anymd"] } } }

VS Code

Install in VS Code with one click, or from a terminal:

code --add-mcp '{"name":"anymd","command":"npx","args":["-y","@sylphx/anymd"]}'

or in .vscode/mcp.json:

{ "servers": { "anymd": { "type": "stdio", "command": "npx", "args": ["-y", "@sylphx/anymd"] } } }

Claude Desktop

One click: download anymd- .mcpb from the latest release and open it. Or, by hand:

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{ "mcpServers": { "anymd": { "command": "npx", "args": ["-y", "@sylphx/anymd"] } } }

Windsurf, Zed, Cline, and other clients

Any client that speaks MCP over stdio: command npx, args ["-y", "@sylphx/anymd"]. To keep the server inside one folder, add --allow-dir=/path/to/docs.

CLI only

npm install -g @sylphx/anymd     # or run it once with: npx -y @sylphx/anymd <file>

Python wheels carry the same prebuilt binary and are on PyPI as anymd from the first release after publication.

Use npm on Windows, macOS and Linux. On macOS and Linux, Homebrew is also available:

brew install sylphxai/tap/anymd

Docker (amd64 and arm64):

docker run --rm -v "$PWD:/data" ghcr.io/sylphxai/anymd report.pdf > report.md

docker run -i --rm ghcr.io/sylphxai/anymd # MCP server on stdio

Or build it from crates.io (Rust 1.95+, CMake and a C++ compiler; doc-VLM OCR and local ASR are included):

cargo install anymd

npm, pip and Docker ship a prebuilt binary, while cargo install compiles one on your machine.

Benchmarks

AgentDocBench is an open benchmark for document → Markdown conversion for agents: license-clean documents in 12 categories (math papers, two-column papers, financial tables, forms, scans, CJK, slides, spreadsheets, Word, EPUB, HTML), scored on verbatim sentences, text F1, reading order, and table cells, with time and output tokens. Every tool runs on the same kind of GitHub-hosted runner (4 CPUs):

anymddoclingkreuzbergunstructuredmarkitdownmarkerpdftotext
Overall score96.493.081.781.276.871.042.2
Table cells F192.289.938.438.457.260.90.0
Reading order98.894.496.893.985.576.852.0
Docs converted38/3838/3838/3838/3838/3830/3823/38
Time, all docs11.5 s2,432.4 s16.0 s346.5 s75.6 s7,104.5 s0.90 s

The generated leaderboard, per-category scores (including where anymd loses), and method are in the benchmark guide. The corpus, ground truth, adapters, and raw results are in bench/, and the Benchmark workflow reruns everything; new tools can join with a single adapter file.

Image and video extraction

Image and video benchmarks cover page-image OCR and embedded video subtitles separately:

DatasetanymdReference alternative
OmniDocBench v1.6 (1,651 page images), overall ↑21.4 (8.1.0, English tesseract)TeleOCR 96.91 (author-reported, not our same-run measurement)
MP4 mov_text fixtures (4 clips, 12 cues), text/start recall ↑91.7% (8.5.1, 11/12)FFmpeg 100% (12/12)
Matroska SubRip fixtures (4 clips, 12 cues), text/start recall ↑91.7% (8.5.1, 11/12)FFmpeg 100% (12/12)

The image score is historical, not a score for the current OCR model. The video scores were measured on 2026-10-08 using small authored regression datasets, not films or lectures; anymd drops a repeated caption at a different timestamp. They measure embedded subtitle extraction, not visual understanding or ASR. The guide links the alternative selection, limitations, timings, raw outputs and reproduction harness.

MCP tools

anymd exposes four tools.

ToolUse it toKey arguments
`outline`Navigate a heading tree, with node ids and unit/Markdown rangessource, format (json · tree)
`read`Turn a file, URL, or folder into Markdownsource, pages ("1-5,8"), max_tokens (default 20000), cursor, ocr, images (refs · none), revisions (markup · accept · reject), transcript, download_asr_model
`search`Find text across files, folders, and URLsquery, sources, mode (auto · literal · ranked), glob, max_results
`inspect`Go deeper on a PDFoperation: render_page, extract_regions, ocr_pages, structure (JSON with geometry), compare, inspect, cite_check (quote and location support, anymd Pro)

A read answer looks like this:

---

source: papers/attention.pdf title: Attention Is All You Need pages: 15 showing: pages 1-9


Attention Is All You Need

…

|Model|BLEU EN-DE|BLEU EN-FR| |-|-|-| |Transformer (big)|28.4|41.8| …

search answers with one line per hit:

5 matches for "masked language model" (2 files, 31 sections searched)
papers/bert.pdf (5)
  • p.1: …by using a “masked language model” (MLM) pre-training objective, inspired by the Cloze task…
  • p.2: …In addition to the masked language model, we also use a “next sentence prediction” task…

If nothing matches exactly, search falls back to BM25-ranked passages, so a question like "how does bidirectional pretraining work" still finds the right page.

CLI

The same binary is a command-line converter, like MarkItDown but much faster:

anymd report.pdf > report.md                 # a file

anymd deck

只显示了开头部分;其余内容请在仓库中阅读

问 PopNx