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Playwright MCP server

An MCP service designed for deploying HTML content to EdgeOne Pages and obtaining an accessible public URL.

Location services, directions, and place details

MCP server for interacting with Neon Management API and databases

Host the HTML or Markdown pages your AI generates and share each as a link with comments, versioning, and access control. Create, update, and organize pages and read reviewer comments over MCP.

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.

A Serper MCP Server

An MCP server that provides a tool for sending transactional emails via Mailtrap

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Secure file operations with configurable access controls

The Milvus MCP server enables AI applications to interact with Milvus vector databases using natural language commands. It allows AI models to perform vector searches, manage collections, and retrieve data without writing custom database queries. This integration facilitates seamless access to vector data, enhancing the capabilities of AI tools like Claude Desktop and Cursor.

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).

Read-only database access with schema inspection

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.