Puter MCP
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
Fast local full-text search for Windows files (tantivy + jieba), exposed to AI agents over MCP.
使っているAIアプリを選んで、設定をコピーしてください。
チェック 3/3 合格 · あなたのパソコンで動きます
まず:どの AI を使っていますか?
使っているアプリを選ぶと、下に手順が表示されます。
Claude はまだ dowse につなげません
提供元はまだ MCP Registry にインストール方法を載せていません
ChatGPT はまだ dowse につなげません
提供元はまだ MCP Registry にインストール方法を載せていません
Claude Code はまだ dowse につなげません
提供元はまだ MCP Registry にインストール方法を載せていません
Cursor はまだ dowse につなげません
提供元はまだ MCP Registry にインストール方法を載せていません
VS Code はまだ dowse につなげません
提供元はまだ MCP Registry にインストール方法を載せていません
cargo: dowseこのサーバーのつなぎ方と、各アプリの公式ドキュメントに基づきます。2026年11月10日に確認。
パソコン上で動かせるアプリ
GooseLM StudioMsty StudioCherry StudioVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月16日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント ltspace で確認済みです
コードの最終更新:14 日前
Apache-2.0を使用
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
質問、コツの共有、使えないことの報告ができます。「役に立った」の多い順です。
まだレビューはありません
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック3項目のうち3項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は14 日前、最新バージョン(0.10.0)がレジストリに届いたのは4 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにRustも必要です。
同じカテゴリには:Puter MCP, XcodeBuildMCP, Anki MCP Server, Sosumi, Zapier
同じカテゴリでほかの作者のもの。コマンドは確認済み。公式を先に、次にスター順。
カードの見方:↓ 週あたりのダウンロードi★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Puter MCP enables AI tools to interact with Puter: manage files, websites, workers, and more
XcodeBuildMCP provides tools for Xcode project management, simulator management, and app utilities.
MCP server for Anki flashcards: adaptive review, notes, media, and deck management via AnkiConnect.
Search and read Apple developer documentation, HIG, and WWDC transcripts as Markdown.
Hosted MCP server connecting AI assistants to 9,000+ apps and 40,000+ actions via Zapier.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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dowse
Open-source local file content search for Windows. Find file names, PDF and Office contents, source code, and text inside screenshots — one hotkey away.
Website · English product page · Windows file-content search guide(中文)
The name comes from a dowsing rod.
No Windows tool satisfies all three of the following at once:
The closest open-source implementation is sist2, but it targets Linux (on Windows it only runs via Docker), treats Chinese text as trigrams, and the project is no longer maintained. dowse is a Windows-native implementation built around these three points.
| 🔍 File name search | Instant, as you type |
| 📄 Document content search | Plain text, Markdown, code, and document formats (PDF, Word, Excel, PowerPoint) |
| 🖼️ Screenshot / image OCR | Text inside PNG/JPG/WebP/BMP images, fully offline (Windows.Media.Ocr) |
| 🈶 Chinese word segmentation | jieba + BM25 ranking, not trigrams — plus automatic GBK encoding detection |
| ⚡ Incremental indexing | File-watch during runtime, mtime/size reconciliation at startup |
| 🤖 MCP server | Exposes local search to AI agents over stdio |
| 🚀 NTFS fast path | MFT enumeration + USN Journal, admin-only, falls back transparently otherwise |
| dowse | Everything | Windows Search | sist2 | |
|---|---|---|---|---|
| File name search | ✓ | ✓ | ✓ | ✓ |
| Document content search | ✓ (persistent index) | on-demand scan | depends on indexed locations and filters | ✓ |
| Screenshot / image OCR | ✓ | ✗ | device/version dependent | limited (optional Tesseract) |
| Proper Chinese segmentation | ✓ (jieba) | — | limited | ✗ (trigrams) |
| Fully local, no network | ✓ | ✓ | ✓ | ✓ |
| Global hotkey overlay | ✓ | ✓ | ✓ (Win key) | ✗ (web UI) |
| Windows-native | ✓ | ✓ | ✓ | ✗ (Linux-first, Docker on Windows) |
path:reports, mtime:>2026-01-01, size:>10mb, uppercase OR between groups, -term to exclude.Design targets; exceeding them is treated as a defect. "Measured" is a from-scratch benchmark of dowse 0.7.0 (i7-13700K / 24 logical cores / 64GB RAM, single machine, single session, 2026-07-12), reusing the byte-identical corpus from the v0.6.1 round-3 benchmark for direct comparability. Full raw output (index/search logs, JSON result files) is kept with the benchmark working directory, outside this repo.
| Metric | Design target | Measured (v0.7.0, 2026-07-12) |
|---|---|---|
| Hotkey to window visible | < 50ms | not measured — CLI-only benchmark, no overlay-app instrumentation |
| Keystroke to results rendered | < 80ms | not measured — same |
| OCR, single image | ~112ms / 1080p screenshot | ~170ms isolated (480×200 synthetic image), unchanged from v0.6.1 — the OCR pipeline was not modified this release. Sub-30ms readings on immediate repeat runs of the same image reflect OS-level recognition caching, not real recognition, and are excluded here. Not real 1080p screenshots |
| Resident memory | < 150MB idle | not measured (idle); peak working set during full-corpus indexing was ~327MB — a different metric, not a regression against the idle target |
| Installer size | < 50MB (revised 2026-07 — the original 15MB target predates the bundled CLI sidecar) | 14.98MB (dowse-app_0.9.0_x64-setup.exe, published release) |
| Full-text index build, 10,000 files / 437MB | seconds (planned filename-only fast path) | 10.0–10.6s — current full-content dowse index, not the planned filename-only MFT path |
| Full-text index build + OCR, 15,100 files (incl. 5,100 images) | — | ~46.6s first pass, all 5,100/5,100 images OCR'd in that same pass — no pending, no second pass needed |
| Search latency, P50 (5 required categories) | — | 30.7–161.1ms across single word / Chinese phrase / English phrase / multi-word AND / zero-result, on a 15,100-document index |
| Search latency, P95 | — | 39.1–172.3ms, same 5 categories |
ext: filter query latency | — | P50 155.6ms, same band as the non-zero-result query categories |
| Index size ÷ corpus size | — | 0.36 (text-only), down from 0.54 in the v0.6.1 round |
Full-corpus rows measured on the same 10,000-file / 437.66MB text corpus plus 5,100 synthetic 480×200 OCR images (89.8MB) used for the v0.6.1 round-3 numbers above — byte-identical, reused directly rather than regenerated. Indexing is roughly 2x faster and the on-disk text index roughly a third smaller than v0.6.1; both track the new tokenizer (lowercase normalization, alphanumeric-boundary splitting of Latin words) producing a leaner term dictionary. The zero-result query dropped from 135ms (v0.6.1) to a startup-noise-level 31ms, consistent with less index to scan before concluding a term is absent. OCR recognition speed is unchanged this release, since the pipeline was not touched: single-image recognition stays around 170ms, and the sub-30ms readings on repeated identical images are OS-level caching artifacts, not real recognition. The full-corpus text-plus-OCR pass got faster (83s to 46.6s) from the quicker tokenizer and write path, not from faster recognition.
Download — grab the installer from the latest release (dowse-app_*_x64-setup.exe), run it, then Alt+\` to summon.
The installer is unsigned, so Windows SmartScreen will flag it on first run. To proceed, click More info and then Run anyway. A code-signing certificate is a recurring cost that is hard to justify for an independent project; it may be reconsidered for a future release.
Install the CLI — the library and command-line tool ship as one dowse package:
cargo install dowse # once published to crates.io
cargo install --path crates/dowse # from a local checkout
Build from source:
git clone https://github.com/ltspace/dowse && cd dowse
cargo run -p dowse -- index D:\docs # build the index cargo run -p dowse -- search 限流 # search cargo run -p dowse -- search "精确短语" # phrase query cargo run -p dowse -- add E:\projects # add another root incrementally (no full rebuild) cargo run -p dowse -- rules show # view index rules (excluded dirs, extra extensions, size cap)
cd crates/dowse-app npm install cargo tauri build # produces the installer under target/release/bundle
Overlay app: Alt+\ to summon, ↑↓ to select, Enter to open, Ctrl+Enter to reveal in Explorer, Ctrl+C to copy path, Esc to hide. Results are paginated in groups of 50; the compact ‹ 1 / N › control shows only when needed, and pressing ↓ past the last result or ↑ before the first moves between pages. Two nearly invisible dropdowns sit at the right of the search bar — file type filter (Ctrl+P) and sort order (Ctrl+S, relevance / newest / oldest / largest); both stay faint until you select a non-default value. Right-click a result row for a native Explorer-style context menu (open / reveal in folder / copy path / copy name). A pin toggle at the top-right keeps the window open when it loses focus (session-only, resets on restart). With an empty input, the overlay lists your recent searches (last 10, stored locally) — ↑↓/Enter to reuse one, Delete to remove it. Ctrl+,` opens the settings panel — general (hotkey rebinding, autostart, interface language) and index rules (excluded directories, extra text extensions, per-file size cap).
dowse mcp starts a read-only MCP server over stdio, exposing the local index to AI agents:
claude mcp add --scope user dowse -- dowse mcp
Four tools: search (query, bounded limit / offset, sort by relevance / mtime / size, comma-separated ext filter, and a total_hits count), preview (long context + metadata for one hit), read_file_chunk (bounded, paginated text from the indexed snapshot), and index_status (document count, index health, active index rules). The server never touches the index writer or opens arbitrary paths — it only reloads the reader before each call, so it can run alongside the overlay app or a live dowse watch session without write contention.
┌─────────────────────────────────────────┐
│ dowse │ │ library core: tantivy index · jieba │ │ segmentation · encoding detection · │ │ text extraction (txt/md/pdf/code/ │ │ docx/xlsx/pptx) · OCR pipeline │ │ ───────────────────────────────────── │ │ CLI + MCP