Power your agent
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Host a copy with our keys and logs.
uvx n24q02m-wet==3.21.0Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
OPENROUTER_API_KEY=<secret> GITHUB_TOKEN=<secret> claude mcp add io.github.n24q02m-wet -- uvx n24q02m-wet==3.21.0รันในเทอร์มินัล
{ "mcpServers": { "io.github.n24q02m-wet": { "command": "uvx", "args": [ "n24q02m-wet==3.21.0" ], "env": { "OPENROUTER_API_KEY": "<secret>", "GITHUB_TOKEN": "<secret>" } } }}claude_desktop_config.json
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รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดจากผู้เผยแพร่
Renamed (2026-09-13): repo is now
wet— CLI-first (wetcommand). PyPI package stayswet(PyPI policy blocks new projectwet); MCP server is a secondary surface: runwetwith no subcommand.
mcp-name: io.github.n24q02m/wet
Open-source MCP server for AI agents: web search, content extraction, and library docs.
Renamed: the repo, CLI and Python module are now
wet. The PyPI distribution stayswet-mcp(installpip install wet-mcp/uvx wet-mcp). The MCP server remains available: runwetwith no subcommand (bare = MCP passthrough); the legacywet-mcpconsole alias still works.
Open-source MCP server for AI agents: web search, content extraction, and library docs.
Current release: v3.x.
media(action="analyze")was removed in the v2.0.0 BREAKING release. Useimagine-mcp'sunderstandaction for vision/audio/video analysis. Seedocs/migration.mdfor the upgrade recipe.
SEARCH_BACKENDSn24q02m-web-core ScrapingAgent (basic_http -> tls_spoof -> render backends from BROWSER_BACKENDS (native / browserless / cf-browser-rendering) -> optional key-gated captcha), 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 mappinganalyze was removed in v2.0.0 -- use imagine-mcp.understand for vision/audio inference[models.embed|rerank|chat|jev_score] cells in ~/.wet/config.toml (OpenRouter default; one OPENROUTER_API_KEY serves every cell)# Method 1 (default): plugin install via Claude Code
/plugin marketplace add n24q02m/claude-plugins
/plugin install wet@n24q02m-plugins
# Method 2 (CLI): direct uvx invocation
claude mcp add wet -- uvx wet-mcp
# Method 3 (source-built container for HTTP / multi-device / OAuth)
docker build --target http -t wet:local .
docker run -d --name wet-http -p 8084:8080 \
-v wet-data:/data -e PUBLIC_URL=https://wet.example.com \
wet:local
# Method 4 (remote): point a client at an HTTP deployment
claude mcp add --transport http wet https://<your-host>/mcp
Install matrix (stdio unless noted; see the Setup page for full steps):
Public OCI image publication is discontinued. Existing historical registry tags remain untouched; new container deployments build from source or use the Cloudflare-managed registry.
The HTTP endpoint speaks Streamable HTTP and is OAuth-gated -- your client is prompted to authenticate in the browser on first connect (no API key to paste). Stand one up via Method 3 or the Deploy to Cloudflare section.
Full setup matrices live at the canonical docs site mcp.n24q02m.com/servers/wet/setup/ and the paste-to-agent snippets at claude-plugins/plugins/wet/setup-with-agent.md (per Spec F single source of truth).
Two supported ways to run the always-listening HTTP server. In both, the MCP
endpoint is http://127.0.0.1:8000/mcp (Streamable HTTP) and the config root
is ~/.wet/ (config.toml, docs.db, subs/). Never run the server as a
spawned subprocess of a client — register the endpoint in your client instead.
git clone https://github.com/n24q02m/wet && cd wet
uv run wet config init # writes ~/.wet/config.toml (default: auth = "no-auth")
uv run wet # serves http://127.0.0.1:8000/mcp
no-auth refuses non-loopback binds — the server is localhost-only until you
switch to token auth.
# 1. Mint a token and its scrypt hash (hull-core canonical; the hash command
# never echoes the token itself)
openssl rand -hex 32 # the token — give it to clients, keep it secret
uv run wet token hash <token> # paste the output as token_hash
# 2. Instance config from the example
cp docker-config/config.example.toml docker-config/config.toml
# edit [server] token_hash and the [models.*] cells (see below)
# 3. Data continuity (optional): carry an existing instance over by copying
# docs.db and subs/ into the wet-home named volume BEFORE first start:
docker volume create wet-wet-home && \
docker cp ~/.wet/docs.db wet-wet-home:/docs.db && \
docker cp ~/.wet/subs wet-wet-home:/subs # adjust: files land as root, chown 999:999
docker compose up -d --build
Compose publishes 127.0.0.1:${WET_PORT:-8000} → container 8000 (loopback
only) and mounts docker-config/config.toml read-only at
/home/appuser/.wet/config.toml. Persistent data (docs.db, subs/) lives in
the wet-home named volume; the download/cache dir in wet-data. Editing the
config takes effect on docker compose restart.
# CLI (same token, JSON-RPC over HTTP not needed — use the installed CLI):
uvx wet-mcp --help # or `wet <tool>` for direct tool calls
# MCP client (Claude Code):
claude mcp add --transport http wet http://127.0.0.1:8000/mcp \
--header "Authorization: Bearer <token>"
# Raw probe:
curl -s -o /dev/null -w '%{http_code}\n' http://127.0.0.1:8000/mcp \
-X POST -H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' # 401
curl -s -o /dev/null -w '%{http_code}\n' http://127.0.0.1:8000/mcp \
-X POST -H 'Authorization: Bearer <token>' -H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"curl","version":"0"}}}' # 200
The [models.embed|rerank|chat|jev_score] cells in ~/.wet/config.toml are
per-task (each its own base_url + api_key + model, OpenAI-spec). The
example ships OpenRouter examples; any OpenAI-compatible endpoint works —
cloud, or a local server such as Ollama (base_url = "http://host.docker.internal:11434/v1" from the container). Keys in the file
are host-only material; alternatively leave api_key = "" and inject at start
via HULL_EMBED_API_KEY / HULL_RERANK_API_KEY / HULL_CHAT_API_KEY /
HULL_JEV_SCORE_API_KEY.
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, configure
the per-task provider cells in ~/.wet/config.toml. Operational settings are
plain environment variables (no app prefix) -- in the HTTP self-host mode they
are entered through the browser setup form instead.
Provider cells -- [models.embed|rerank|chat|jev_score] each take their own
base_url + api_key + model (OpenAI-spec). OpenRouter is the pre-wired
default for every cell; any OpenAI-compatible endpoint works. Cell keys may be
left empty in the file and injected at start via HULL_EMBED_API_KEY /
HULL_RERANK_API_KEY / HULL_CHAT_API_KEY / HULL_JEV_SCORE_API_KEY. When a
cell keeps the OpenRouter default base_url, OPENROUTER_API_KEY in the
environment serves it as a last-resort credential — one OpenRouter key can
power every task. Empty embed/rerank cells fall back to the local ONNX models;
an unkeyed chat cell disables LLM features (e.g. extract(action="agent")).
FASTRETRIEVAL_CACHE_PATH controls the local model cache.
Search backends -- SEARCH_BACKENDS is an ordered runtime fallback chain:
searxng (default, local or external via SEARXNG_URL), keyed tavily / brave /
exa / kagi / openrouter, optional-key firecrawl, and credential-free duckduckgo /
startpage. Keyed providers use TAVILY_API_KEY, BRAVE_API_KEY, EXA_API_KEY,
KAGI_API_KEY, or OPENROUTER_API_KEY (comma-separate for rotation). The
openrouter backend runs the query through a chat completion with the
openrouter:web_search server tool and maps the returned url_citation
annotations onto the shared result shape; OPENROUTER_MODEL (default a
free-tier model), OPENROUTER_BASE_URL, and OPENROUTER_SEARCH_ENGINE
override its behavior. Firecrawl attempts a keyless request when FIRECRAWL_API_KEY
is absent; rejection or a DuckDuckGo/Startpage bot challenge advances the chain.
Result reranking uses the configured rerank cell ([models.rerank] — cloud via OpenRouter by default, or local ONNX when unkeyed); the same chain serves web search, research, similar-page search, agent search, and docs discovery/indexing fallbacks. SearXNG retains its science-category filter for research; other providers use their own search capabilities. Hosted users configure the chain and keys in their own relay record. An empty hosted record never inherits an operator's provider key or local SearXNG URL; single-user stdio still uses env/settings and preserves the public local path.
Browser render backends -- BROWSER_BACKENDS (CSV, escalation chain) picks
the headless render leg of extract: native (in-process chromium, the
zero-config default), browserless (self-host render service -- set
BROWSERLESS_URL + BROWSERLESS_TOKEN), and cf-browser-rendering (Cloudflare
Browser Rendering -- set CF_ACCOUNT_ID + CF_BROWSER_RENDERING_TOKEN). Empty
chain falls back to native. Set CAPSOLVER_API_KEY to append an optional,
key-gated CAPTCHA tier as the last escalation step.
Robots policy -- set RESPECT_ROBOTS_TXT=true to enforce robots.txt
across both the extract strategy chain and the Crawl4AI-backed crawl,
sitemap, and list_media actions. The default is false to preserve existing
deployment behaviour; configure this process-level policy explicitly when the
operator requires robots enforcement.
Invisible tier + seeded identity (opt-in) -- append invisible to
BROWSER_BACKENDS to add a stealth-Firefox engine tier as the last escalation
step (n24q02m-hull[invisible] extra: pip install "wet-mcp[invisible]"). One
coherent browser identity spans the whole chain: seed it with WET_IDENTITY_SEED
(or let wet derive once and persist it under ~/.wet/subs/<sub>/identity.json)
and install the wet[identity] extra. Without the extras the chain keeps
legacy behaviour (warn-once). STEALTHFOX_BINARY points at a patched Firefox
build to skip the engine download; IDENTITY_PROFILE_DIR (default
~/.wet/subs/<sub>/profiles) keeps persistent browser profiles per namespace.
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 (default true), GOOGLE_DRIVE_CLIENT_ID
(required for sync), SYNC_FOLDER (default wet), SYNC_INTERVAL (default
300s). Sync uses Google Drive over the OAuth Device Code flow (no browser
redirect).
DOCS_DB_BACKEND=cf-d1 disables GDrive/S3 file sync, including automatic
startup, relay wizard and device-code setup, even if legacy sync settings remain.
Non-CF SQLite deployments retain GDrive sync; SYNC_S3_BUCKET selects S3
instead, and SYNC_ENABLED=false disables both.
HTTP self-host -- MCP_TRANSPORT=http, PUBLIC_URL=<your-domain>. The setup
form is gated by MCP_RELAY_PASSWORD; multi-user deployments require
CREDENTIAL_SECRET (per-user vault key), MCP_JWT_SIGNING_SECRET (rotatable
OAuth JWT key), and MCP_DCR_SERVER_SECRET.
Example stdio config (one OpenRouter key powers every provider cell):
{
"mcpServers": {
"wet": {
"command": "uvx",
"args": ["wet-mcp"],
"env": {
"OPENROUTER_API_KEY": "sk-or-xxx"
}
}
}
}
To use a different endpoint or model per task, edit the [models.*] cells in
~/.wet/config.toml, or inject HULL_EMBED_API_KEY / HULL_RERANK_API_KEY /
HULL_CHAT_API_KEY / HULL_JEV_SCORE_API_KEY at start instead of storing keys
in the file.
Stable architecture with two transports: stdio (default, local) and
HTTP (self-host, OAuth-gated). No daemon-bridge layer and no auto-spawn
from stdio. The media.analyze action was removed in the v2.0.0 BREAKING
release -- see docs/migration.md for the upgrade
recipe. Current release line: v3.x.
Full docs at mcp.n24q02m.com/servers/wet/setup/:
In-repo references (Spec F single source of truth: setup docs live in claude-plugins/plugins/wet/):
docs/ARCHITECTURE.md -- web-core ScrapingAgent integration, strategy chain, storage layout, LLM provider dispatchdocs/BENCHMARKS.md -- v1.x baseline coverage / latency placeholders + tier-1 fixture metricsInstall with AI agent -- paste this to your AI coding agent:
Install MCP server
wetfollowing the steps at https://raw.githubusercontent.com/n24q02m/claude-plugins/main/plugins/wet/setup-with-agent.md
6 MCP tools (3 domain + config + help + config__open_relay). The legacy
setup tool merged into config action dispatch.
Media boundary: For vision / audio understanding (image captioning, OCR, audio transcription, video summarization), use imagine-mcp.
media.analyzewas removed in wet v2.0.0 -- useimagine-mcp.understandinstead.
The package installs two console scripts: wet (primary) and wet-mcp
(legacy alias kept so existing uvx wet-mcp configs keep working). A bare
invocation (or any leading-dash flag) starts the server; a leading positional
argument is dispatched as a subcommand.
uvx --from wet-mcp wet warmup # try a subcommand without a persistent install
wet # start the server over stdio (default transport)
wet --http # start the server over Streamable HTTP (self-host mode)
wet auth google # authorize the Google credential provider for Drive sync
wet logout # clear the local Google Drive sync token
wet warmup # pre-download local models + run auto-setup (SearXNG, browser) to avoid first-run delays
wet docs reindex <library> # drop the cached docs index for <library>; the next docs search re-indexes it
auth google accepts an optional bring-your-own OAuth client via --client-id
and --client-secret (single-user / local machine only; the token is written to
the local store). Each subcommand prints a JSON result and exits.
CONVERT_ALLOWED_DIRS restrictiongit clone https://github.com/n24q02m/wet.git
cd wet
uv sync
uv run wet
Run your own single-user wet instance serverless on Cloudflare (Containers + D1 + Vectorize + KV).
Prerequisites: a Cloudflare account on the Workers Paid plan — required for Containers, D1, and Vectorize (the Cloudflare free tier does not include them) — and the wrangler CLI.
git clone https://github.com/n24q02m/wet && cd wetwrangler loginwrangler 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
wrangler.jsonc.docker build --target http --build-arg SLIM=1 -t wet:beta .
wrangler containers push wet:beta # prints registry.cloudflare.com/<ACCOUNT_ID>/wet:beta
wrangler secret put CREDENTIAL_SECRET
wrangler secret put MCP_JWT_SIGNING_SECRET
wrangler secret put MCP_RELAY_PASSWORD
wrangler secret put MCP_DCR_SERVER_SECRET
wrangler secret put CF_BROWSER_RENDERING_TOKEN
wrangler deploy and complete setup in the browser relay form at your Worker domain.Storage maps to Cloudflare via MCP_STORAGE_BACKEND=cf-kv (credentials/tokens, encrypted),
DOCS_DB_BACKEND=cf-d1 (docs + BM25 full-text), and Vectorize (embeddings). The
default headless renderer is BROWSER_BACKENDS=cf-browser-rendering. Local
ONNX fallbacks are disabled in the slim image; configure search and cloud
retrieval through each authenticated subject's relay record. Worker-wide
search/model chains and provider keys are not forwarded to the container.
The per-task [models.*] cells (in config.toml) configure cloud
embed/rerank/chat: point each cell's base_url at any OpenAI-compatible
endpoint — OpenRouter is the default — and supply the key via the cell's
api_key or the matching HULL_<TASK>_API_KEY env. One OPENROUTER_API_KEY
serves every cell that keeps the OpenRouter default base_url.
Store keyed search-provider credentials (such as TAVILY_API_KEY) in the same
subject record. Provision Vectorize and EMBEDDING_DIMS for a dimension
supported by the selected embedding model.
Every tagged release deploys automatically (only while the CF_DEPLOY_ENABLED
gate is on -- see the pause note below): the CD deploy-cf job checks out
the released tag, builds the http-slim image, pushes it to the Cloudflare-managed
registry as immutable :<release-tag>, deploys the Worker, and gates on a canary
health check -- a release is live at exactly its own version. A beta dispatch
redeploys the beta; a stable dispatch is maintainer-gated. Manual wrangler deploy
against the maintained instance is not permitted: it would break the
release-tag ↔ live-image correspondence. Self-hosting on your own Cloudflare
account (the button above) is unaffected.
Paused 2026-09-13: the CF deploy token was removed from the account as off-manifest (process violation), so
deploy-cfnow no-ops behind theCF_DEPLOY_ENABLEDrepo variable. The maintained instance stays frozen at its last deployed release until a token is re-established via the documented process and the variable is set totrue.
wet ships a smithery.yaml so it can be installed and run
through Smithery. The manifest declares a stdio start
command (uvx --python 3.13 wet-mcp) with an empty config schema -- no config is
required to start, and providers and credentials are configured at runtime via
the server's own config flow (see Configuration).
This plugin implements TC-Local (machine-bound, single trust principal). See mcp-core trust model for full classification.
Apache-2.0 -- See LICENSE.
Open-source MCP server for AI agents: web search, content extraction, and library docs.
{ "mcpServers": { "io.github.n24q02m-wet": { "command": "uvx", "args": [ "n24q02m-wet==3.21.0" ], "env": { "OPENROUTER_API_KEY": "<secret>", "GITHUB_TOKEN": "<secret>" } } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ผู้เผยแพร่ระบุตัวแปรสภาพแวดล้อมเหล่านี้: OPENROUTER_API_KEY, GITHUB_TOKEN
Wet ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-15 Open-source MCP server for AI agents: web search, content extraction, and library docs.