AILANG Parse

io.github.sunholo-data由 GitHub 账号 sunholo-data 发布v0.14.1更新于 Nov 5, 2026

Parse, convert and create Office and PDF documents. OAuth at /mcp/connect/, API key at /mcp/.

发布方自己的服务：Parse

已验证Streamable HTTP可网页运行效率与工作流文件与存储

概览

AI 生成的概览

把 Office、PDF 和图片文档解析为结构化块、Markdown 或 HTML，并可在九种格式间转换或新建文档。

可以做什么
  • 解析文档为结构化块、Markdown、HTML、A2UI 或简短摘要
  • 在多种格式间转换文档，并从 Markdown 生成新文档
  • 通过 JSON 编辑增量修改 Office 文档
  • 通过选择文件卡片或一次性上传链接上传文件
  • 估算解析成本与耗时，并查看支持的格式和示例文档
  • 查看账户套餐、用量、密钥与请求历史设置，并提交匿名反馈
适用场景
适合需要读取 DOCX、PPTX、XLSX、PDF 或图片并获得干净结构化或 Markdown 输出的场景，也适合根据 Markdown 生成报告、幻灯片或表格。文档处理较多的工作流值得添加，一般聊天用途则不必。
运行要求
可使用托管端点，或本地 npm/PyPI 包（npx 或 uvx 运行）。托管代理接口可选 API 密钥（DOCPARSE_API_KEY 或 Authorization Bearer 头）；没有密钥时通过设备流工具登录，浏览器客户端走 OAuth 连接路径。本地命令行处理 PDF 需要 poppler，扫描件还需额外安装后端。
安装前请注意
上传的文件会发送到托管服务，约 30 分钟内可复用后删除；请求历史是否保留取决于账户设置。API 密钥属于机密。PDF 与图片的 AI 后端为手动开启，可能产生付费模型用量。编辑与转换会写出新文档，反馈提交会进入人工审核队列。

安装

在 SourceWeft 中

  1. 打开 控制台中的 AILANG Parse，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.sunholo-data%2Fparse/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "ailang-parse": {
      "type": "http",
      "url": "https://docparse.ailang.sunholo.com/mcp/connect/"
    }
  }
}

README

AILANG Parse

[AILANG Registry] [PyPI] [npm] [Go] [MCP Registry] [CI]

Universal document parsing and generation in AILANG. Extracts structured content from DOCX, PPTX, XLSX, PDF, and image files into JSON and markdown — and writes documents back out in 9 formats. To author a document, write Markdown and convert it; see Writing documents in Markdown.

Office formats (DOCX, PPTX, XLSX) use deterministic XML parsing — no AI, no cloud, instant results. PDFs default to the deterministic pdftotext backend (poppler) — also no AI, no cloud — with poppler (headings, lists, tables and header/footer from the same tools, no installs), docling and liteparse as local alternatives escalating automatically to local docling OCR for scanned/image-only PDFs. Pluggable AI (Gemini, Claude, local Ollama) is available via --pdf-backend ai but is never chosen automatically. Images delegate to whatever AI model you plug in. AILANG Parse is AI-agnostic: swap --pdf-backend/--ai to change the backend, zero code changes.

Install

Two routes; both put a docparse command on your PATH, and both write output next to the file you parse (--output-dir DIR or DOCPARSE_OUTPUT_DIR to change that).

Already have AILANG? (0.42.0+ of this package, AILANG v0.40.0 dev or later)

bash
ailang install sunholo/ailang_parsedocparse report.docx

The package's [bin] table gives you a shim in ~/.ailang/bin that runs straight out of the registry cache: parse one file, or --convert it. It is deliberately the thin path — describe/summarize, batch mode, PDF backends and --install-backends are not on it (see below). ailang bin list / ailang bin uninstall docparse manage it.

Otherwise, or for the full CLI:

bash
curl -fsSL https://www.sunholo.com/ailang-parse/install.sh | sh

Fetches the published package (~400 KB), installs the AILANG runtime if you do not have it, and links the full docparse wrapper (plus docparse-audit and docparse-render) into ~/.local/bin. --version, --prefix and --uninstall are supported; re-running is a no-op. If you install both, ailang install tells you which one docparse resolves to.

That covers every deterministic format. PDF needs two more things, and they are easy to miss:

bash
brew install poppler        # pdftotext, the default PDF backend                            # (apt install poppler-utils on Debian/Ubuntu)docparse --install-backends # docling + liteparse, for scans and layout

--install-backends matters even if you never pass --pdf-backend: when pdftotext finds no text layer the parser escalates to docling on its own, so without it a scanned PDF fails on the default backend. AI backends authenticate with Google ADC (gcloud auth application-default login), not an API key.

Contributors: install from a clone instead
bash
git clone https://github.com/sunholo-data/ailang-parse.gitln -s "$(pwd)/ailang-parse/bin/docparse" /usr/local/bin/docparse

The wrapper finds the project root by walking up for docparse/main.ail, so it works from a clone, an installed prefix, or a symlink chain. Note that the CLI wrapper and the PDF adapter live under assets/ — the only path the AILANG publisher bundles verbatim — with symlinks at their historical locations.

SDKs

Use AILANG Parse from your language of choice:

bash
pip install ailang-parse          # Pythonnpm install @ailang/parse         # JavaScript/TypeScriptgo get github.com/sunholo-data/ailang-parse-go  # Go

Quick Start

bash
# Office documents (deterministic, no AI needed)docparse report.docxdocparse slides.pptxdocparse spreadsheet.xlsx
# PDF (deterministic pdftotext by default — no AI); images (AI auto-enabled)docparse document.pdfdocparse photo.png
# Optionsdocparse report.docx describe        # AI image descriptionsdocparse report.docx summarize       # AI document summarydocparse contract.pdf                # PDF: deterministic pdftotext (default)docparse scan.pdf                    # Scanned PDF: escalates to local docling OCRdocparse scan.pdf --pdf-backend ai --ai gemini-3.5-flash-lite  # External AI (paid), opt-in only
# Format conversiondocparse report.docx --convert output.htmldocparse data.csv --convert report.docxdocparse notes.md --convert slides.pptxdocparse notes.md --convert offer.docx --reference-doc letterhead.docx
# AI document generationailang run --entry main --caps IO,FS,Env,AI --ai gemini-3.5-flash-lite \  docparse/main.ail --generate report.docx --prompt "Q1 sales report with tables"

Output

Every run produces, in the directory you ran from (--output-dir DIR or DOCPARSE_OUTPUT_DIR to change it):

  • <filename>.json — Structured JSON with typed blocks
  • <filename>.md — LLM-ready markdown

What AILANG Parse Extracts

FeatureDOCXPPTXXLSXBest Competitor
Tables with merged cellsYesYesYesRaw OOXML only
Track changes (redlining)Yes——Pandoc (3/3)
Comments (interleaved)Yes——Raw OOXML (2/2)
Headers/footersYes——Kreuzberg (2/3)
Text boxes / VML shapesYesYes—Raw OOXML (1/2)
Equations (§22.1)Yes——None
Field codes (§17.16)Yes——Kreuzberg, OOXML
Speaker notes—Yes—None
Multi-sheet extraction——YesKreuzberg

OfficeDocBench v2 (87 files, 11 formats, 8 metrics): AILANG Parse PR #116 candidate 98.1% composite with 100.0% coverage. Ten adapters compared, including the hosted Extend (Parse 2.0, 53.3% coverage-adjusted) and LlamaParse (standard mode) APIs. Source-verified structural assertions now measure cell positions, comment targets, list depth, formatting, formulas, colours, and slide/notes relationships. Methodology and results.

Supported Formats

Parsing (16 formats): DOCX, PPTX, XLSX, ODT, ODP, ODS, HTML, Markdown, CSV, EPUB, EML, MBOX, TEX, RTF, PDF, images (JPG/PNG)

Generation (9 formats): DOCX, PPTX, XLSX, ODT, ODP, ODS, HTML, Markdown, QMD (Quarto)

Writing documents in Markdown

Markdown is the input an LLM can write, so it is the practical way to generate a document: write markdown, convert to any of the nine output formats.

bash
docparse report.md --convert report.docx

What survives the trip: YAML front matter (title/author/date → document properties), bold/italic/code/strike as real character formatting, links as real hyperlinks, images (local paths are read and embedded), fenced code blocks, blockquotes, nested lists, thematic breaks, and tables with alignment and column spans.

Headers, footers, comments and tracked changes have no Markdown syntax; those are preserved when converting from a document that already contains them.

Local document verification (v0.41.2+)

docparse-audit and docparse-render are AILANG companions to the CLI. AILANG Parse v0.41.2 requires AILANG 0.35.0 or newer. They run locally and require no hosted account. The installer links them when present in the installed release; in a checkout use ./bin/docparse-audit and ./bin/docparse-render.

bash
docparse-audit report.docx --strictdocparse-render report.docx --output-dir qa/reportdocparse-render before.docx --compare after.docx --output-dir qa/comparison

The read-only DOCX audit emits JSON inventories and findings for table grids and headers, heading gaps, image descriptions, links, comments and fields. Exit codes are 0 for a completed audit, 1 for findings with --strict, and 2 for unreadable input. It is a bounded structural check; it does not certify accessibility or resolve arbitrary style inheritance.

Rendering supports .docx, .odt, .pptx, .xlsx and .pdf. Install LibreOffice and Poppler for Office input; PDF needs only Poppler. No new Python dependency is needed. Select tools with --soffice / --pdftoppm (or DOCPARSE_SOFFICE / DOCPARSE_PDFTOPPM). --dpi defaults to 144 and --timeout to 120 seconds per process. The output directory must be new. Each document gets a PDF and numbered PNG pages; the manifest records source hashes, tool paths and page directories. Comparison identifies changed/added/removed pages using uncompressed raster hashes. Inspect the two PNG sets side by side; no diff overlays are generated. visual_review: pending means page images still need inspection.

The implementation is in docparse/services/document_render.ail and docparse/services/docx_audit.ail; regression verification is AILANG too:

bash
ailang run --entry main --caps IO,FS,Env,Process \  --process-allowlist "true,false,mktemp,mkdir,$(command -v soffice),$(command -v pdftoppm)" \  --process-timeout 120s scripts/verify_document_quality.ail -- \  /tmp/docparse-quality-new "$(command -v soffice)" "$(command -v pdftoppm)"

Styling a generated DOCX from a template

--reference-doc is the Quarto/Pandoc reference-doc feature: an existing .docx supplies the look, the Markdown supplies the content.

bash
docparse annex.md --convert annex.docx --reference-doc letterhead.docx

Generated tables use the selected section's usable width, with bounded content weights for columns and repeating semantic headers. Merged-cell widths sum the columns they cover.

The template's styles.xml, numbering.xml, theme, embedded fonts, headers, footers and page setup are applied to the new content. Everything the merge does not regenerate is carried through byte-for-byte, so the letterhead, logo and licensed fonts come out exactly as they went in.

What comes from where:

Templatepage size, margins, headers, footers, page numbering, fonts, theme, colours
Your documentthe body content, and docProps/core.xml (title/author)
Mergedstyles.xml (ours fill only the styleIds the template lacks), numbering.xml (our list definitions take ids above the template's), [Content_Types].xml, both .rels

Two consequences worth knowing:

  • The template's headers and footers win. A source document's own headers are dropped rather than mixed with the letterhead. The page furniture all lives in the template's body <w:sectPr>, which is lifted whole.
  • The template's comments are dropped along with its body, and so are commentsExtended.xml and people.xml. Comments in the source document still come through.

Two flags refine a multi-section template:

  • --reference-section N picks which of the template's sections supplies the page setup, headers and footers — 1 is the first section, Word's numbering. The default is the last section (the body-level one, what the flag-less behaviour has always lifted). A multi-section template's wanted furniture is often an earlier section's — the master agreement's CONFIDENTIAL footer, not the Annex's missing one.
  • --table-style NAME binds generated tables to a table style the template defines (matched on styleId, then style name). Without it, the style named Table is used if the template has one, else the first table style that is not the implicit Normal Table. Under a bound style the generator stops emitting its own hardcoded borders — the style carries them.

An unreadable or non-DOCX reference is an error and writes nothing — a silent fallback to the built-in styling would produce a plausible file missing exactly the letterhead it was asked for. DOCX output only.

Architecture

docparse/├── types/document.ail           # Block ADT (11 variants)├── services/│   ├── format_router.ail        # Format detection (36 inline tests)│   ├── zip_extract.ail          # ZIP layer (9 inline tests)│   ├── docx_parser.ail          # DOCX XML → Blocks (6 inline tests)│   ├── pptx_parser.ail          # PPTX slides → Blocks│   ├── xlsx_parser.ail          # XLSX worksheets → Blocks│   ├── direct_ai_parser.ail     # PDF/image → Blocks (AI)│   ├── layout_ai.ail            # AI self-healing (optional)│   ├── output_formatter.ail     # JSON + markdown output│   └── docparse_browser.ail     # WASM browser adapter└── main.ail                     # CLI entry point

91 contracts, 50+ inline tests. Of the 91, Z3 proves 14 outright; the rest are checked at runtime under --prove/--verify-contracts in CI, and skip statically because parser code is recursive and higher-order, which is outside Z3's decidable fragment.

AI Configuration

AILANG Parse uses AILANG's AI effect — any model AILANG supports works:

bash
docparse scan.pdf --ai gemini-3.5-flash-lite          # Google (default; fast)docparse scan.pdf --ai granite-docling           # Local Ollama (free)docparse scan.pdf --ai claude-haiku-4-5          # Anthropic

AI usage is bounded by capability budgets (AI @limit=200 on main), so costs are predictable.

Dev Commands

bash
docparse --check       # Type-check all modulesdocparse --test        # Run inline testsdocparse --prove       # Static Z3 contract verification

Benchmarks

bash
uv run benchmarks/run_benchmarks.py --suite office     # Structural (no API, instant)uv run benchmarks/run_benchmarks.py --suite pdf         # PDF extraction (needs AI)uv run benchmarks/run_benchmarks.py --competitors       # Compare to Docling etc.

See benchmarks/ for details.

License

Apache 2.0

来源：README.md，提交 b48b321

工具

10

createUpload

Create upload link

Get one-time upload links for files in your sandbox or workspace, when you can run shell commands with network access (Claude Code, Codex, a code sandbox). One token PER FILE: a token uploads exactly one file. For several files set count (1..20) to get that many descriptors in one call. Each descriptor has a curl line: run it where the file is (replace <path>); it prints a receipt with a fileRef. Then call mcpParse, mcpConvert or editDocument with that fileRef. Tokens work once, for 10 minutes. If curl fails with a network error, or the user wants to pick a file, call uploadDocument instead, or ask for an https:// link. Never parse the file yourself.

输入参数countfilenamemimeType

editDocument

Edit document

Edit a document: parse it, apply a JSON array of edit deltas, and return the modified blocks (same shape as mcpParse with outputFormat "blocks"). deltas "" or "[]" returns the document unchanged. Office formats only: docx, pptx, xlsx, odt, odp, ods. Give the document ONE way: fileRef: a file the user uploaded (mcp-file://docparse/file_...). file: in ChatGPT, the file the user attached (ChatGPT fills it in). content + filename: the file base64-encoded, contentEncoding="base64", e.g. filename "contract.docx". filepath: an https:// URL of a publicly reachable document, or a sample id from mcpFormats such as "sample_docx_formatting". To get a file back, pass the edited blocks' Markdown to mcpConvert. Which way to send a file: a file in your sandbox or workspace: call createUpload (one token PER FILE; set count for several) and run its curl line, then pass the fileRef here. curl fails with a network error, or the user wants to pick a file: call uploadDocument (the user chooses it in the AILANG Parse card). The card parses the file; it does not convert or edit it. a public https:// link; short text as content. In ChatGPT, file. Never parse the file yourself (your own script, or your own reading of it) and present that as this tool's result: if the file cannot be sent, tell the user.

输入参数filedeltascontentfileReffilenamefilepathcontentEncoding

mcpAccount

Account, quota and usage

Your AILANG Parse account: plan, monthly usage and limits, your keys, and whether request history is kept. Actions: "status" (default) → plan, usage this month and remaining allowance "keys" → your keys with per-key usage ("usage" is an alias) "history" → whether request history (parsed output kept for replay) is on for your account "history_on" / "history_off" → change it for the account and all its keys

输入参数action

mcpConvert

Convert document

Create a document in another format: docx, pptx, xlsx, odt, odp, ods, html, md or qmd. To write a new document, write Markdown and send it as content with filename "notes.md" (headings become slides in pptx). To convert an existing document, give it ONE way: fileRef: a file the user uploaded (mcp-file://docparse/file_...). file: in ChatGPT, the file the user attached (ChatGPT fills it in). content + filename: the document itself; binary files base64-encoded with contentEncoding="base64", text as plain text. input: an https:// URL of a publicly reachable document, or a sample id from mcpFormats. Which way to send a file: a file in your sandbox or workspace: call createUpload (one token PER FILE; set count for several) and run its curl line, then pass the fileRef here. curl fails with a network error, or the user wants to pick a file: call uploadDocument (the user chooses it in the AILANG Parse card). The card parses the file; it does not convert or edit it. a public https:// link; short text as content. In ChatGPT, file. Never parse the file yourself (your own script, or your own reading of it) and present that as this tool's result: if the file cannot be sent, tell the user. The result carries download_url, a link to the generated file valid for one hour: give it to the user. It also carries the file as base64 in content (encoding "base64"; text formats come back as utf8). Leave outputPath empty: the server cannot write to your filesystem.

输入参数fileinputcontentfileReffilenameoutputPathoutputFormat必填contentEncoding

mcpEstimate

Estimate cost and latency

Estimate cost and latency for parsing a document. Accepts a file path or sample_id. No auth required.

输入参数filepath必填outputFormat必填

mcpFormats

List formats and capabilities

What AILANG Parse can read and write, how to send it a document, and the sample documents you can try without one. No sign-in needed.

无需输入参数。

mcpParse

Parse document

Parse a document into structured blocks, Markdown, HTML or A2UI, or a short summary of it. Give the document ONE way: fileRef: an uploaded file (mcp-file://docparse/file_...). The same user can use it again for 30 minutes after the first use (more parts, another format), then it is deleted. file: in ChatGPT, the file the user attached (ChatGPT fills it in). filepath: an https:// URL of a publicly reachable document, or a sample id from mcpFormats such as "sample_docx_formatting". content + filename: short text only (Markdown, HTML, CSV); the filename's extension picks the parser. outputFormat: blocks (default), markdown, html, a2ui, or summary (counts, the first 50 headings, warnings: a few KB). Look with summary first when a document may be large. Large documents come in parts: when the result has page.nextCursor, call mcpParse again with the same fileRef/filepath and cursor=<nextCursor> for the next part; read every part before answering about the whole document. maxChars sets the part size as a string of digits (default "80000", "2000".."120000"). pdfBackend: leave empty for PDFs with a text layer. Set "ai" when the result is PDF_TEXT_LAYER_SPARSE (a scanned PDF): each page is read by an AI model, about 5 seconds a page, and the call counts as an AI request. Pass the same pdfBackend with every cursor call. Which way to send a file: a file in your sandbox or workspace: call createUpload (one token PER FILE; set count for several) and run its curl line, then call this tool with the fileRef the upload prints. curl fails with a network error, or the user wants to pick a file: call uploadDocument; the user chooses it in the AILANG Parse card, which uploads it and gives you its fileRef. a public https:// link: filepath. Short text: content. Never parse the file yourself (your own script, or your own reading of it) and present that as this tool's result: if the file cannot be sent, tell the user.

输入参数filecursorcontentfileReffilenamefilepathmaxCharsrequestIdpdfBackendoutputFormatcontentEncoding

submit_feedback

Send feedback

Anonymous bug report / feature request / docs gap, queued for human review. Default routing: `public-feedback` inbox (general AILANG). Pass `package="vendor/name"` (e.g. "sunholo/auth") to route to that package's `pkg:vendor/name` inbox where its autonomous agent watches. Categories: bug, feature, docs, limitation. Body limit 10KB, snippet limit 4KB. Optional contact field for follow-up; opaque to the server. Set `auto_dispatch=true` to authorize the package agent to act on your submission immediately (default false — files for human triage; pkg-feedback agent template lands in a separate sprint).

输入参数body必填title必填contactpackagesnippetcategory必填auto_dispatchailang_version必填

uploadDocument

Upload a document

Let the user pick a file to parse: shows the AILANG Parse card with a Choose file button. The card uploads the file and tries to summarise it for you; either way you get its fileRef. Then call mcpParse with that fileRef for the content (the same user can reuse it for 30 minutes; a large document comes in parts). Use this when the user wants to pick a file, or when createUpload's curl fails with a network error. For a file already in your sandbox or workspace, use createUpload + curl instead. Never parse the file yourself.

输入参数outputFormat

uploadViaHost

Receive upload from widget

Receive a file from the AILANG Parse upload widget, relayed by the host as base64. Only the widget calls this; ticket is the upload token it holds.

输入参数base64必填ticket必填filename必填

版本历史

3
  1. v0.14.1最新Nov 5, 2026
  2. v0.14.0Nov 3, 2026
  3. v0.13.1Sep 16, 2026

评分

已满足适用检查项的 100%

该评分衡量此收录条目的完整程度，依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价，也不表示服务器安全或当前可用：可连通性是带日期的检查结果，评分也不包含安装量或用户评价。

  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分

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