wet-mcp

# WET - Web Extended Toolkit MCP Server

mcp-name: io.github.n24q02m/wet-mcp

**Open-source MCP server for AI agents: web search, content extraction, and library docs.**

| Phase | Status | Scope |
|---|---|---|
| Phase 1 | Shipped | web-core ScrapingAgent migration, smart chunks output, search polish, media slim |
| Phase 2 | Shipped | Context7-level docs search: library index (Tier 1 + Tier 2), version-aware queries with token cap, project lock (Cabinets) |
| Phase 3 | **Shipped** | `extract.agent` multi-step research with cited synthesis, `extract.interact` click/fill/submit via patchright (optional session persistence), `docs_004_chunk_summaries` migration, **`media.analyze` removed (v2.0.0)** |

> **Current release: v3.x.** `media(action="analyze")` was removed in the
> v2.0.0 BREAKING release. Use
> [`imagine-mcp`](https://github.com/n24q02m/imagine-mcp)'s
> `understand` action for vision/audio/video analysis. See
> [`docs/migration.md`](docs/migration.md) for the upgrade recipe.

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[![Python](https://img.shields.io/badge/Python-3776AB?logo=python&logoColor=white)](#)
[![SearXNG](https://img.shields.io/badge/SearXNG-3050FF?logo=searxng&logoColor=white)](#)
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<!-- BEGIN: AUTO-GENERATED-CROSS-PROMO -->
<details>
  <summary><strong>Sister projects from n24q02m</strong> (click to expand)</summary>

| Project | Tagline | Tag |
|---|---|---|
| [agent-chat-plugin](https://github.com/n24q02m/agent-chat-plugin) | Peer AI agents chat in a shared folder — no human relay, no orchestrator, wor... | Tooling |
| [better-code-review-graph](https://github.com/n24q02m/better-code-review-graph) | Knowledge graph for token-efficient code reviews -- semantic search and call-... | MCP |
| [better-drive](https://github.com/n24q02m/better-drive) | 2-way Google Drive sync with .driveignore filter — rclone engine, Windows tray | Tooling |
| [better-email-mcp](https://github.com/n24q02m/better-email-mcp) | IMAP/SMTP email for AI agents -- read, send, organize folders, and manage att... | MCP |
| [better-godot-mcp](https://github.com/n24q02m/better-godot-mcp) | Composite MCP server for Godot Engine -- 17 composite tools for AI-assisted g... | MCP |
| [better-notion-mcp](https://github.com/n24q02m/better-notion-mcp) | Markdown-first Notion for AI agents -- pages, databases, blocks, and comments... | MCP |
| [better-semantic-release](https://github.com/n24q02m/better-semantic-release) | Drop-in python-semantic-release fork with built-in release-safety guards (orp... | Tooling |
| [better-telegram-mcp](https://github.com/n24q02m/better-telegram-mcp) | Telegram for AI agents -- messages, chats, media, and contacts across both bo... | MCP |
| [better-workspace-mcp](https://github.com/n24q02m/better-workspace-mcp) | Google Workspace MCP server (Docs/Drive/Calendar/Gmail/Sheets/Slides/Tasks/Ch... | MCP |
| [claude-plugins](https://github.com/n24q02m/claude-plugins) | Claude Code plugin marketplace for the n24q02m MCP servers -- install web sea... | Marketplace |
| [imagine-mcp](https://github.com/n24q02m/imagine-mcp) | Image and video understanding + generation for AI agents -- across Gemini, Op... | MCP |
| [jules-task-archiver](https://github.com/n24q02m/jules-task-archiver) | Chrome Extension for bulk operations on Jules tasks via batchexecute API -- a... | Tooling |
| [mcp-core](https://github.com/n24q02m/mcp-core) | Shared foundation for building MCP servers -- Streamable HTTP transport, OAut... | MCP |
| [mnemo-mcp](https://github.com/n24q02m/mnemo-mcp) | Persistent AI memory with hybrid search and embedded sync. Open, free, unlimi... | MCP |
| [fastretrieval](https://github.com/n24q02m/fastretrieval) | Multi-model embedding and reranking runtime via ONNX and GGUF | Library |
| [skret](https://github.com/n24q02m/skret) | Secrets without the server. | CLI |
| [tacet](https://github.com/n24q02m/tacet) | A self-distilling neuro-symbolic cascade that amortises LLM cost across knowl... | Tooling |
| [web-core](https://github.com/n24q02m/web-core) | Shared web infrastructure package for search, scraping, HTTP security, and st... | Library |
| [wet-mcp](https://github.com/n24q02m/wet-mcp) | Open-source MCP server for AI agents: web search, content extraction, and lib... | MCP |

</details>
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## Table of contents

- [Features](#features)
- [Status](#status)
- [Quick install](#quick-install)
- [Configuration](#configuration)
- [Documentation](#documentation)
- [Tools](#tools)
- [CLI](#cli)
- [Comparison](#comparison)
- [Security](#security)
- [Build from Source](#build-from-source)
- [Deploy to Cloudflare](#deploy-to-cloudflare)
- [Smithery](#smithery)
- [Trust Model](#trust-model)
- [License](#license)



<a href="https://glama.ai/mcp/servers/n24q02m/wet-mcp">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/n24q02m/wet-mcp/badge" alt="WET MCP server" />
</a>

## Features

- **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 via `SEARCH_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 via `n24q02m-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 mapping
- **Local File Conversion** -- Convert PDF, DOCX, XLSX, CSV, HTML, EPUB, PPTX to Markdown
- **Media** -- List + download images / videos / audio files. `analyze` was removed in v2.0.0 -- use `imagine-mcp.understand` for 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 the `EMBEDDING_MODELS` / `RERANK_MODELS` / `LLM_MODELS` model chains for higher-quality vectors and LLM features
- **Sync** -- Cross-machine sync of indexed docs via Google Drive (OAuth Device Code, no browser redirect)

## Quick install

```bash
# 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 (source-built container for HTTP / multi-device / OAuth)
docker build --target http -t wet-mcp:local .
docker run -d --name wet-mcp-http -p 8084:8080 \
  -v wet-data:/data -e PUBLIC_URL=https://wet.example.com \
  wet-mcp:local

# Method 4 (remote): point a client at an HTTP deployment
claude mcp add --transport http wet https://<your-host>/mcp
```

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](#deploy-to-cloudflare) section.

Full setup matrices live at the canonical docs site
[mcp.n24q02m.com/servers/wet-mcp/setup/](https://mcp.n24q02m.com/servers/wet-mcp/setup/)
and the paste-to-agent snippets at
[claude-plugins/plugins/wet-mcp/setup-with-agent.md](https://github.com/n24q02m/claude-plugins/blob/main/plugins/wet-mcp/setup-with-agent.md)
(per Spec F single source of truth).

## Configuration

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

| Env var | Task | Empty default |
|---|---|---|
| `EMBEDDING_MODELS` | Embeddings for docs search | Local fastretrieval ONNX |
| `RERANK_MODELS` | Result reranking | Local fastretrieval cross-encoder |
| `LLM_MODELS` | `extract(action="agent")` synthesis | LLM features disabled |

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

| Model prefix | Key env var |