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Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

Generate a PDF or Excel document from HTML (document_content) or a URL (document_url). Exactly one of document_content / document_url is required. By default the document is HOSTED and the tool returns a { download_url } you can fetch — ideal for agents (no large binary in the response). Set hosted:false to get the raw document back as base64, or async:true to enqueue a job and poll docraptor_get_document_status. IMPORTANT: real documents consume account credits (billed). Set test:true to generate a FREE, watermarked document while developing. DocRaptor API: POST /docs.

Saves an email to the Mail.app Drafts folder for the user to review and send manually — never sends. Composes a new draft (pass `to`/`subject`/`body`), or a reply draft (pass `reply_to_message_id` plus `body`). On a multi-account Mac, pass `account` (an account name from list_email_accounts) or `from` (a sender address) to place the draft in that account's Drafts; otherwise it lands in the default account. Attach files by passing `attachments` (comma-separated absolute file paths, e.g. a PDF quote) — they are attached to the saved draft. Use this for the cautious user who wants AI-composed mail but insists on sending it themselves.

Reads a text file from the local filesystem. Supports .txt, .md, .csv, .json, .xml, .log, .yaml, .toml and common code file types. For PDFs use pdf_read, for Word use word_read, for Excel use excel_read.

Reads a text file from the synced Google Drive folder (.txt, .md, .csv, .json, code files...). Note: native Google Docs/Sheets/Slides sync as .gdoc/.gsheet pointers, not real files — export them from Drive or read Office/PDF copies instead. Auto-detects UTF-8 with Latin-1/CP1252 fallback.

Reads and extracts text from a PDF file.

Extract a PDF into clean Markdown and structured elements (headings, paragraphs). Returns the canonical ExtractedDocument object. mode "hybrid" runs a heavier semantic engine that also reconstructs tables and bounding boxes; the default "fast" engine is prose-only (low confidence.tables).

Split a PDF into semantic chunks ready for embeddings (RAG). Each chunk carries its text, estimated tokens, starting page, section heading and the source element ids for citation.

Return every table in a PDF as structured JSON (reconstructed rows and columns) with page and bounding box for verifiable citations. Uses the semantic (hybrid) engine.

Publish a PDF as a tracked doc.page Doc Link and get back a shareable URL. The link belongs to the API key's account and also appears in its doc.page library. Requires an API key. Free plan: up to 3 active links; custom vanity slugs are premium-only. Optional expiry and open-notification toggle.

把 PDF / Word(.docx) / PPT(.pptx) / HTML / 纯文本 解析成干净 Markdown + 结构化（按实际解析页数计费，每页 1 credit）。 纯解析 + 格式转换、不生成任何 AI 内容；扫描件 OCR 暂未支持。 支持 page_range 只解析指定页码段（只按解析的页数扣费）。

抽取文档中的表格（PDF / .docx / .pptx），转成 headers+rows 的 JSON 可直接计算。 与 doc_parse 的区别：表格不再压平成 markdown 竖线文本，而是结构化行列， AI 可直接算合计/对比；PDF 用版面识别找表格。

抽取文档内嵌的图片（PDF / .docx / .pptx），打包 ZIP 落文件中转站并返下载 URL。 过滤 <1KB 的图标噪声；多模态 AI 可配合 doc_parse 的文本一起理解图文文档。

把 Markdown 渲染成 PDF 文件，存进你的文件中转站并返回下载 URL（2 credit/次）。 纯本地渲染（markdown + weasyprint），中文正常显示；不生成任何 AI 内容。失败自动退款。

Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.