wet-mcp

- Web Search-- Embedded SearXNG metasearch (Google, Bing, DuckDuckGo, Brave) with query expansion, TTL cache (1 h general / 5 min time-sensitive), standardized citation format, and 200-token snippet cap. Optional cloud search backends (Tavily, Brave, Exa) as a fallback chain viaSEARCH_BACKENDS
- Academic Research-- Search Google Scholar, Semantic Scholar, arXiv, PubMed, CrossRef, BASE
- Library Docs-- Auto-discover and index documentation with FTS5 hybrid search, HyDE-enhanced retrieval, and version-specific docs
- Content Extract-- 5-strategy escalation chain vian24q02m-web-coreScrapingAgent(basic_http->tls_spoof-> render backends fromBROWSER_BACKENDS(native/browserless/cf-browser-rendering) -> optional key-gatedcaptcha), markitdown bridge for low-tier HTML/MD fallback, smart chunks structured output (clean text + markdown + JSON-LD + code blocks + metadata), batch processing (up to 50 URLs), deep crawling, site mapping
- Local File Conversion-- Convert PDF, DOCX, XLSX, CSV, HTML, EPUB, PPTX to Markdown
- Media-- List + download images / videos / audio files.analyzewas removed in v2.0.0 -- useimagine-mcp.understandfor vision/audio inference
- Anti-bot-- Stealth strategies bypass Cloudflare, Medium, LinkedIn, Twitter
- Zero Config-- Built-in local reference embedding + reranking through fastretrieval, no API keys needed. Optional cloud providers (Jina AI, Gemini, OpenAI, Cohere, xAI, Anthropic) selected per task via theEMBEDDING_MODELS/RERANK_MODELS/LLM_MODELSmodel chains for higher-quality vectors and LLM features
- Sync-- Cross-machine sync of indexed docs via Google Drive (OAuth Device Code, no browser redirect)

# Method 1 (default): plugin install via Claude Code /plugin marketplace add n24q02m/claude-plugins /plugin install wet-mcp@n24q02m-plugins # Method 2 (CLI): direct uvx invocation claude mcp add wet -- uvx wet-mcp # Method 3 (recommended for HTTP / multi-device / OAuth) docker run -d --name wet-mcp-http -p 8084:8080 \ -v wet-data:/data -e MCP_TRANSPORT=http \ -e PUBLIC_URL=https://wet.example.com \ n24q02m/wet-mcp:latest # Method 4 (remote): point a client at an HTTP deployment claude mcp add --transport http wet https://<your-host>/mcp

wet runs zero-config out of the box: web search uses an embedded local SearXNG, and embedding/reranking fall back to the bundled local ONNX models through fastretrieval when no cloud keys are set. For higher-quality results, point each task at a cloud model chain. All settings are plain environment variables (no app prefix) -- in the HTTP self-host mode they are entered through the browser setup form instead.

Model chains(CSVprovider/model,provider/model; order = fallback). Leave a chain empty to use the local ONNX models (embedding/rerank) or to disable LLM features (LLM):

Provider keys-- the provider is inferred from each model's prefix; supply the matching key (litellm<PROVIDER>_API_KEYconvention):

FASTRETRIEVAL_CACHE_PATHcontrols the local model cache. The oldQWEN3_EMBED_CACHE_PATHname is still honored when the new name is absent.

Search backends--SEARCH_BACKENDS(CSV, runtime fallback chain) oversearxng(default, local) plus optional cloud providerstavily/brave/exa. Point at an external SearXNG withSEARXNG_URL. Cloud providers needTAVILY_API_KEY/BRAVE_API_KEY/EXA_API_KEY.

Browser render backends--BROWSER_BACKENDS(CSV, escalation chain) picks the headless render leg ofextract:native(in-process chromium, the zero-config default),browserless(self-host render service -- setBROWSERLESS_URL+BROWSERLESS_TOKEN), andcf-browser-rendering(Cloudflare Browser Rendering -- setCF_ACCOUNT_ID+CF_BROWSER_RENDERING_TOKEN). Empty chain falls back tonative. SetCAPSOLVER_API_KEYto append an optional, key-gated CAPTCHA tier as the last escalation step.

Robots policy-- setRESPECT_ROBOTS_TXT=trueto enforcerobots.txtacross both theextractstrategy chain and the Crawl4AI-backedcrawl,sitemap, andlist_mediaactions. The default isfalseto preserve existing deployment behaviour; configure this process-level policy explicitly when the operator requires robots enforcement.

Disable local fallbacks-- opt out of the heavy in-process local fallbacks per capability (e.g. on a slim container that renders/searches/embeds via cloud backends only):DISABLE_LOCAL_BROWSER,DISABLE_LOCAL_SEARCH,DISABLE_LOCAL_EMBED,DISABLE_LOCAL_RERANK.

Docs sync--SYNC_ENABLED(defaulttrue),GOOGLE_DRIVE_CLIENT_ID(required for sync),SYNC_FOLDER(defaultwet-mcp),SYNC_INTERVAL(default300s). Sync uses Google Drive over the OAuth Device Code flow (no browser redirect).

HTTP self-host--MCP_TRANSPORT=http,PUBLIC_URL=<your-domain>. The setup form is gated byMCP_RELAY_PASSWORD; multi-user deployments also requireCREDENTIAL_SECRET(per-user vault key) andMCP_DCR_SERVER_SECRET.

Install with AI agent-- paste this to your AI coding agent:

6 MCP tools (3 domain +config+help+config__open_relay). The legacysetuptool merged intoconfigaction dispatch.

Thewet-mcpconsole script starts the server and also exposes a few one-shot operator subcommands. A bare invocation (or any leading-dash flag) starts the server; a leading positional argument is dispatched as a subcommand.

wet-mcp # start the server over stdio (default transport) wet-mcp --http # start the server over Streamable HTTP (self-host mode) wet-mcp auth google # authorize the Google credential provider for Drive sync wet-mcp logout # clear the local Google Drive sync token wet-mcp warmup # pre-download local models + run auto-setup (SearXNG, browser) to avoid first-run delays wet-mcp docs reindex <library> # drop the cached docs index for <library>; the next docs search re-indexes it

auth googleaccepts an optional bring-your-own OAuth client via--client-idand--client-secret(single-user / local machine only; the token is written to the local store). Each subcommand prints a JSON result and exits.

- SSRF prevention-- URL validation on crawl targets
- Graceful fallbacks-- Cloud → Local embedding, multi-tier crawling
- Error sanitization-- No credentials in error messages
- File conversion sandboxing-- OptionalCONVERT_ALLOWED_DIRSrestriction

git clone https://github.com/n24q02m/wet-mcp.git cd wet-mcp uv sync uv run wet-mcp

Run your own single-user wet instance serverless on Cloudflare (Containers + D1 + Vectorize + KV).

Prerequisites:a Cloudflare account on theWorkers Paid plan— required for Containers, D1, and Vectorize (the Cloudflare free tier does not include them) — and thewranglerCLI.
- git clone https://github.com/n24q02m/wet-mcp && cd wet-mcp
- wrangler login
- Provision resources and apply the D1 schema:

wrangler d1 create wet-docs wrangler d1 execute wet-docs --file migrations/0001_init_wet.sql --remote wrangler d1 execute wet-docs --file migrations/0002_project_context.sql --remote wrangler d1 execute wet-docs --file migrations/0003_version_index_state.sql --remote wrangler vectorize create wet-docs-vectors --dimensions 768 --metric cosine wrangler kv namespace create wet-kv

docker pull ghcr.io/n24q02m/wet-mcp:beta docker tag ghcr.io/n24q02m/wet-mcp:beta wet-mcp:beta wrangler containers push wet-mcp:beta # prints registry.cloudflare.com/<ACCOUNT_ID>/wet-mcp:beta

wrangler secret put CREDENTIAL_SECRET wrangler secret put JINA_AI_API_KEY wrangler secret put GOOGLE_VERTEX_EXPRESS_API_KEY wrangler secret put XAI_API_KEY wrangler secret put MCP_RELAY_PASSWORD wrangler secret put MCP_DCR_SERVER_SECRET wrangler secret put SEARXNG_URL wrangler secret put BROWSERLESS_URL # render backend (BROWSER_BACKENDS default = browserless,cf-browser-rendering) wrangler secret put BROWSERLESS_TOKEN wrangler secret put CF_BROWSER_RENDERING_TOKEN

Storage maps to Cloudflare viaMCP_STORAGE_BACKEND=cf-kv(credentials/tokens, encrypted),DOCS_DB_BACKEND=cf-d1(docs + BM25 full-text), and Vectorize (embeddings). Web search uses a SearXNG instance (SEARCH_BACKEND=searxng,SEARXNG_URL) or Tavily (SEARCH_BACKEND=tavily); embed/rerank are forced cloud viaEMBEDDING_MODELS/RERANK_MODELS.

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