Discover verified Model Context Protocol servers to extend your AI assistants. Install configs for Claude Code, Cursor, VS Code, and more.
Browse 260 MCP servers across databases, communication platforms, cloud providers, DevOps pipelines, analytics services, and version control systems. Each server entry includes verified compatibility badges, GitHub star counts, transport type, install commands, and security notes. The Model Context Protocol (MCP) is an open standard that lets AI assistants like Claude, Cursor, and GitHub Copilot interact with external tools and data sources through a unified interface — find the right server to connect your AI workflow to the services you already use.
Context7 provides up-to-date, version-specific documentation and code examples directly in your AI coding context. Instead of hallucinated APIs from outdated training data, Context7 fetches real documentation for libraries like React, Next.js, Prisma, and 1000+ more. It resolves version conflicts by letting you specify exact library versions, ensuring code suggestions use current APIs. The server indexes official docs, README files, and code examples, making them searchable and retrievable through natural language queries within your AI assistant.
Serena is an advanced code intelligence MCP server that provides semantic code understanding beyond simple text search. It builds a full semantic graph of your codebase — understanding function calls, type hierarchies, import chains, and symbol relationships. AI assistants can query this graph to find all callers of a function, trace data flow, identify dead code, and understand complex inheritance patterns. Serena works with TypeScript, Python, Java, Go, and Rust, providing IDE-level intelligence to terminal-based AI coding agents.
Codebase Memory provides persistent memory for AI coding assistants across sessions. It stores architectural decisions, code conventions, dependency choices, and project-specific context that persists between conversations. When you start a new chat with your AI assistant, it automatically loads relevant project context — no need to re-explain your tech stack, naming conventions, or architectural patterns. The server uses local vector storage for efficient retrieval and supports team-shared memories for collaborative development.
