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.
Elige la app de IA que usas y copia la configuración.
Pasó 3/3 revisiones · Funciona en tu computadora
Primero: ¿qué IA usas?
Elige tu app y los pasos aparecen abajo.
Claude todavía no puede conectar dowse
El autor aún no ha puesto cómo instalarlo en el registro MCP
ChatGPT todavía no puede conectar dowse
El autor aún no ha puesto cómo instalarlo en el registro MCP
Claude Code todavía no puede conectar dowse
El autor aún no ha puesto cómo instalarlo en el registro MCP
Cursor todavía no puede conectar dowse
El autor aún no ha puesto cómo instalarlo en el registro MCP
VS Code todavía no puede conectar dowse
El autor aún no ha puesto cómo instalarlo en el registro MCP
Otras apps que usan MCP, como Windsurf, Zed o Gemini CLI, añaden servidores de forma distinta. Míralo en la lista de apps
Ver todas las apps que usan MCP →
La configuración JSON está en “Para desarrolladores”, abajo
cargo: dowseSegún cómo se conecta este servidor y la documentación de cada app, comprobado el 10 nov 2026.
Se ejecutan en tu computadora en
GooseLM StudioMsty StudioCherry StudioVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 15 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub ltspace, dueña del repo
Último cambio de código: hace 13 días
Usa Apache-2.0
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
No revisado: solo se pueden revisar paquetes npm y PyPI con versión
Pregunta, comparte un consejo o avisa de que no funciona. Ordenado por lo que la gente marcó como útil.
Aún no hay publicaciones. Sé la primera persona en preguntar o compartir.
Lo publicado aquí lo escriben usuarios, no el equipo de PopNx.
Aún no hay reseñas
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 3 de 3 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez hace 13 días, y la última versión (0.10.0) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Rust en tu computadora.
En la misma categoría: Puter MCP, XcodeBuildMCP, Anki MCP Server, Sosumi, Zapier
Misma categoría, otros creadores, comandos ya revisados. Primero los oficiales, luego por estrellas.
Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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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