WET - Web Extended Toolkit MCP Server

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

Web search, content extraction, and library docs for AI agents -- 5-strategy scraping, runs without API keys.

| 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's understand action for vision/audio/video analysis. See docs/migration.md for the upgrade recipe.

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| Project | Tagline | Tag | |---|---|---| | better-code-review-graph | Knowledge graph for token-efficient code reviews -- semantic search and call-... | MCP | | better-email-mcp | IMAP/SMTP email for AI agents -- read, send, organize folders, and manage att... | MCP | | better-godot-mcp | Composite MCP server for Godot Engine -- 17 composite tools for AI-assisted g... | MCP | | better-notion-mcp | Markdown-first Notion for AI agents -- pages, databases, blocks, and comments... | MCP | | better-telegram-mcp | Telegram for AI agents -- messages, chats, media, and contacts across both bo... | MCP | | claude-plugins | Claude Code plugin marketplace for the n24q02m MCP servers -- install web sea... | Marketplace | | imagine-mcp | Image and video understanding + generation for AI agents -- across Gemini, Op... | MCP | | jules-task-archiver | Chrome Extension for bulk operations on Jules tasks via batchexecute API -- a... | Tooling | | mcp-core | Shared foundation for building MCP servers -- Streamable HTTP transport, OAut... | MCP | | mnemo-mcp | Persistent AI memory with hybrid search and embedded sync. Open, free, unlimi... | MCP | | qwen3-embed | Lightweight Qwen3 text embedding and reranking via ONNX Runtime and GGUF | Library | | skret | Secrets without the server. | CLI | | tacet | TACET: a self-distilling neuro-symbolic cascade that amortises LLM cost in kn... | Tooling | | web-core | Shared web infrastructure package for search, scraping, HTTP security, and st... | Library | | wet-mcp | Open-source MCP server for AI agents: web search, content extraction, and lib... | MCP |

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Table of contents

• Features
• Status
• Quick install
• Configuration
• Documentation
• Tools
• Comparison
• Security
• Build from Source
• Deploy to Cloudflare
• Trust Model
• 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 -> headless Crawl4AI), 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 Qwen3 embedding + reranking, 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

# 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

Full setup matrices live at the canonical docs site mcp.n24q02m.com/servers/wet-mcp/setup/ and the paste-to-agent snippets at claude-plugins/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 Qwen3 ONNX models 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 Qwen3-Embedding ONNX | | RERANK_MODELS | Result reranking | Local Qwen3-Reranker ONNX | | 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 | Get it at | |---|---|---| | jina_ai/ | JINA_AI_API_KEY | jina.ai/api-key | | gemini/ | GEMINI_API_KEY | aistudio.google.com/apikey | | openai/ (or bare) | OPENAI_API_KEY | platform.openai.com | | cohere/ | COHERE_API_KEY | dashboard.cohere.com | | xai/ | XAI_API_KEY | console.x.ai | | anthropic/ | ANTHROPIC_API_KEY | console.anthropic.com |

Any other litellm provider works via env passthrough -- see litellm provider docs for its key name.

Search backends -- SEARCH_BACKENDS (CSV, runtime fallback chain) over searxng (default, local) plus optional cloud providers tavily / brave / exa. Point at an external SearXNG with SEARXNG_URL. Cloud providers need TAVILY_API_KEY / BRAVE_API_KEY / EXA_API_KEY.

Docs sync -- SYNC_ENABLED (default true), GOOGLE_DRIVE_CLIENT_ID (required for sync), SYNC_FOLDER (default wet-mcp), SYNC_INTERVAL (default 300s). 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 by MCP_RELAY_PASSWORD; multi-user deployments also require CREDENTIAL_SECRET (per-user vault key) and MCP_DCR_SERVER_SECRET.

Example stdio config (cloud chains):

{
  "mcpServers": {
    "wet": {
      "command": "uvx",
      "args": ["wet-mcp"],
      "env": {
        "EMBEDDING_MODELS": "jina_ai/jina-embeddings-v5-text-small",
        "RERANK_MODELS": "jina_ai/jina-reranker-v3",
        "LLM_MODELS": "gemini/gemini-3-flash-preview",
        "JINA_AI_API_KEY": "jina_xxx",
        "GEMINI_API_KEY": "AIza_xxx"
      }
    }
  }
}

Status

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.

Documentation

Full docs at mcp.n24q02m.com/servers/wet-mcp/setup/:

• Setup -- install methods for Claude Code, Codex, Gemini CLI, Cursor, Windsurf, mcp.json
• Modes overview -- stdio / local-relay / remote-relay / remote-oauth
• Multi-user setup -- per-JWT-sub credential model

In-repo references (Spec F single source of truth: setup docs live in claude-plugins/plugins/wet-mcp/):

• docs/ARCHITECTURE.md -- web-core ScrapingAgent integration, strategy chain, storage layout, LLM provider dispatch
• docs/BENCHMARKS.md -- v1.x baseline coverage / latency placeholders + tier-1 fixture metrics

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

Install MCP server wet-mcp following the steps at https://raw.githubusercontent.com/n24q02m/claude-plugins/main/plugins/wet-mcp/setup-with-agent.md

Tools

6 MCP tools (3 domain + config + help + config__open_relay). The legacy setup tool merged into config action dispatch.

| Tool | Description | |:-----|:------------| | search | Web (SearXNG metasearch), news, images, academic research (Scholar / arXiv / PubMed / CrossRef / Semantic Scholar / BASE), library docs (HyDE + FTS5), find similar pages. Includes docs_resolve (library name -> ranked id), docs_query (version-aware + topic + 5000-token cap), docs_lock_project (Cabinets project pin via pyproject / package.json / go.mod / Cargo.toml manifest detection). | | extract | URL -> smart chunks dict (clean_text + markdown + structured_data + code_blocks + metadata) via web-core 5-strategy chain. Batch processing (up to 50 URLs), deep crawling, site mapping, local file conversion (PDF/DOCX/XLSX/PPTX/EPUB), structured extraction (JSON Schema) | | media | list (discover URLs from gallery pages), download (SSRF-safe). analyze was removed in v2.0.0 -- use imagine-mcp.understand instead | | config | status, set, cache_clear, docs_reindex, warmup, setup_sync, setup_status, setup_skip, setup_reset, setup_complete | | help | Per-tool documentation: search, extract, media, config | | config__open_relay | Re-trigger the zero-config relay setup flow (prints a fresh relay URL for the browser form). Registered via mcp-core's register_open_relay_tool so an LLM can restart setup without a manual restart. |

Media boundary: For vision / audio understanding (image captioning, OCR, audio transcription, video summarization), use imagine-mcp. media.analyze was removed in wet v2.0.0 -- use imagine-mcp.understand instead.

Comparison

How wet-mcp stacks up against direct competitors in each pillar:

| Capability | wet-mcp | Brave Search | Tavily | Firecrawl | Context7 | |---|---|---|---|---|---| | Web search | Yes (SearXNG aggregation) | Yes | Yes | No | No | | Extract URL | Yes (5-strategy chain) | No | Yes (basic) | Yes | No | | Media list / download | Yes | No | No | No | No | | Library docs search | Yes (Tier 1 curated + Tier 2 on-demand, version-aware, Cabinets) | No | No | No | Yes | | Academic research | Yes (6 providers) | No | No | No | No | | Self-hostable | Yes | No | No | No | Yes | | Free tier | Yes (open source) | Limited | Limited | Limited | Yes |

Security

• 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 -- Optional CONVERT_ALLOWED_DIRS restriction

Build from Source

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

Deploy to Cloudflare


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

Prerequisites: a Cloudflare account on the Workers Paid plan and the wrangler CLI.

1. git clone https://github.com/n24q02m/wet-mcp && cd wet-mcp
2. wrangler login
3. 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 vectorize create wet-docs-vectors --dimensions 768 --metric cosine
   wrangler kv namespace create wet-kv

Paste the returned IDs into wrangler.jsonc.

1. Push the container image to your Cloudflare managed registry (CF Containers cannot

pull from external registries directly), then set <YOUR_ACCOUNT_ID> in wrangler.jsonc: `` 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 ``

1. Set secrets (use SEARXNG_URL with basic-auth userinfo, e.g.

https://user:pass@searxng.example.com, or TAVILY_API_KEY if you set SEARCH_BACKEND=tavily): `` 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 ``

1. 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). Web search uses a SearXNG instance (SEARCH_BACKEND=searxng, SEARXNG_URL) or Tavily (SEARCH_BACKEND=tavily); embed/rerank are forced cloud via EMBEDDING_MODELS/RERANK_MODELS.

Trust Model

This plugin implements TC-Local (machine-bound, single trust principal). See mcp-core trust model for full classification.

| Mode | Storage | Encryption | Who can read your data? | |---|---|---|---| | stdio (default) | ~/.wet-mcp/config.json | AES-GCM, machine-bound key | Only your OS user (file perm 0600) | | HTTP self-host | Same as stdio | Same | Only you (admin = user) |

License

MIT -- See LICENSE.