Hyperliquid MCP Server v4

Zhipu Web Search MCP Server is a search engine specifically designed for large models. It integrates four search engines, allowing users to flexibly compare and switch between them. Building upon the web crawling and ranking capabilities of traditional search engines, it enhances intent recognition capabilities, returning results more suitable for large model processing (such as webpage titles, URLs, summaries, site names, site icons, etc.). This helps AI applications achieve "dynamic knowledge acquisition" and "precise scenario adaptation" capabilities.

Playwright MCP server

5 AI skills as MCP tools: MBTI relationship sandbox (2-20 people), fact-checking (3 methods: source-tracing, cross-reference, logic-check), deep business analysis (5-layer), game audio brief generator, weekly skill radar. All monetized via Alipay AI Pay (¥0.10-0.50/call).

Retrieving and analyzing issues from Sentry.io

VC follow intelligence for AI agents. Track what top investors follow on X — detect new follows, convergence signals, and trending companies before they're announced.

Create a remote sandbox that can execute code/run commands/upload and download files. 创建远程沙盒，可以执行代码/运行命令/上传下载文件

A Model Context Protocol Server connector for Perplexity API, to enable web search without leaving the MCP ecosystem.

ContextBridge (CB) is a local-first retrieval layer that sits between your codebase and your AI coding agent. Instead of pasting raw source files into context or letting the AI guess which files matter, CB indexes your codebase's real structure (via Graphify) and returns a compact, ranked result — owner file, related files, key symbols, and dependency chains — grounded in your actual code. A typical response is ~4–8 KB instead of the 100+ KB of raw source an AI would otherwise need to read to answer the same question — roughly a 96% cut in input tokens sent to your cloud AI, with zero hallucinated file paths or method names.

Web content fetching and conversion for efficient LLM usage