Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Fast local full-text search for Windows files (tantivy + jieba), exposed to AI agents over MCP.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
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。
作者はレジストリにインストール情報を載せていません。下にある作者のREADMEをご覧ください。
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7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
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
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
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1項目のうち1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–、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:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ 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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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sist2, but it targets Linux (on Windows it only runs via Docker), treats Chinese text as trigrams, and the project is no longer maintained.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
冒頭だけを表示しています。続きはリポジトリで読めます