wet-mcp
★ 19
vs
search-mcp
★ 26
wet-mcp vs search-mcp
wet-mcp: WET - Web Extended Toolkit MCP Server provides AI agents with comprehensive capabilities for web search, content extraction, and library documentation retrieval. It features a 5-strategy scraping chain, integrated metasearch, and zero-configuration local models for embeddings and reranking, operating without requiring API keys.; search-mcp: Parallel Search MCP offers free web search capabilities for coding agents, enabling them to find current documentation, check facts, and read web pages without leaving their environment. It integrates directly with various agent clients, requiring no account or API key for its free tier.
01
TL;DR
wet-mcpRAG / Knowledge Base · ★ 19
AI Agent Research: Enabling AI agents to perform comprehensive web research and academic literature reviews to gather information for complex tasks.
search-mcpRAG / Knowledge Base · ★ 26
Documentation Retrieval: Find official documentation for libraries and frameworks directly within the agent's environment.
02
Side-by-Side Comparison
Field
wet-mcpsearch-mcpCategory
RAG / Knowledge Base
RAG / Knowledge Base
Stars
★ 19
★ 26
License
Apache-2.0
MIT
Install Command
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
codex mcp add parallel-search --url https://search.parallel.ai/mcp
Updated
1d ago
2w ago
Open Source
Yes
Yes
Tags
Web Search, Content Extraction, AI Agent Tooling
Web Search, Agent Tool, LLM Integration
03
Features
wet-mcp01Web Search: Embedded SearXNG metasearch (Google, Bing, DuckDuckGo, Brave) with query expansion, TTL cache, and standardized citation format. Optional cloud search backends.
02Content Extract: 5-strategy escalation chain for URL content extraction, providing smart chunks structured output (clean text, markdown, JSON-LD, code blocks, metadata), batch processing, deep crawling, and local file conversion.
03Library Docs: Auto-discover and index documentation with FTS5 hybrid search, HyDE-enhanced retrieval, and version-specific docs for AI agents.
04Zero Config: Built-in local Qwen3 embedding + reranking, no API keys needed. Optional cloud providers can be selected per task for higher-quality results.
search-mcp01Web Search Tool: `web_search` allows agents to search the web and retrieve relevant excerpts with source links.
02Web Fetch Tool: `web_fetch` enables agents to read specific URLs and obtain page content for detailed analysis.
03Keyless Free Access: Offers anonymous access to its core tools without requiring an account, API key, or login for the free tier.
04Multi-Agent Compatibility: Integrates with a range of coding agents and LLM clients, including Codex, Gemini CLI, Hermes, OpenCode, OpenClaw, Cursor, and VS Code.
05Higher Limits with Authentication: Supports higher usage limits and configurable search behavior for authenticated users via a Parallel account.
04
Use Cases
wet-mcp↳AI Agent Research: Enabling AI agents to perform comprehensive web research and academic literature reviews to gather information for complex tasks.
↳Structured Data Extraction: Automatically extracting structured and unstructured data from web pages, including clean text, markdown, JSON-LD, and code blocks, for further processing by agents.
↳Developer Documentation Access: Providing AI agents with version-aware and topic-specific access to software library documentation for code generation, debugging, or learning.
↳Offline Content Processing: Converting local files (PDF, DOCX, HTML, etc.) to Markdown for AI agent consumption, enabling analysis of local knowledge bases.
search-mcp↳Documentation Retrieval: Find official documentation for libraries and frameworks directly within the agent's environment.
↳Fact Checking: Verify information, such as the latest stable releases of software, by citing official web sources.
↳Web Content Analysis: Read and summarize web pages (e.g., explaining concepts from a given URL or providing context).
↳Contextual Information Gathering: Provide agents with external, up-to-date web data to enhance their understanding and response generation.
FAQ
FAQ
What is the difference between wet-mcp and search-mcp?
Both wet-mcp and search-mcp are in the RAG / Knowledge Base category. wet-mcp has 19 stars, while search-mcp has 26 stars.
Which is better, wet-mcp or search-mcp?
The best choice depends on your use case. Choose wet-mcp if AI Agent Research: Enabling AI agents to perform comprehensive web research and academic literature reviews to gather information for complex tasks., and search-mcp if Documentation Retrieval: Find official documentation for libraries and frameworks directly within the agent's environment..
Is wet-mcp free or open source?
Yes, wet-mcp is open source on GitHub (Apache-2.0).
Is search-mcp free or open source?
Yes, search-mcp is open source on GitHub (MIT).
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