MCP Server for cryptographic hashing

Flag features directly from chat in your code editor, including VS Code, Cursor, Windsurf, Claude Code—any IDE with MCP support.

Repository management, file operations, and GitHub API integration

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.

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

MCP server for ArcGIS Portal and ArcGIS Online. Lets AI assistants search content, query feature layers, manage features, handle content operations, and administer users and groups. Built on the Model Context Protocol for integration with Claude Desktop, Cursor, VS Code Copilot, and other MCP clients. Disclaimer: This is an independent open-source project. It is not affiliated with, endorsed by, or sponsored by Esri Inc. "ArcGIS" is a registered trademark of Esri.

An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.

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GitLab API, enabling project management

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Web content fetching and conversion for efficient LLM usage

A Model Context Protocol (MCP) server for image generation and manipulation using fal.ai's Stable Diffusion model.

Read-only database access with schema inspection

Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.

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.

Official Firecrawl MCP Server - Adds powerful web scraping to Cursor, Claude and any other LLM clients.

Retrieving and analyzing issues from Sentry.io