MCP Servers

Official Elastic MCP server that connects AI agents to Elasticsearch data using the Model Context Protocol. Enables natural language interactions with Elasticsearch indices — querying, analyzing, and retrieving data without custom APIs. Supports both stdio and streamable-HTTP transports, and works with Elasticsearch 8.x/9.x clusters including Elasticsearch Serverless. Distributed as a Docker container image from the Elastic registry.

Official Figma MCP server that brings design context directly into AI coding workflows. Provides tools for extracting design information, generating code from Figma selections, taking screenshots, creating and editing Figma files, generating diagrams from Mermaid syntax, searching design systems, managing Code Connect mappings, and uploading assets. Supports both remote (OAuth) and local (desktop app) server modes.

Official Hugging Face MCP Server that connects AI assistants directly to the Hugging Face Hub ecosystem. Provides tools for searching and retrieving models, datasets, and research papers, running inference on thousands of Gradio-powered AI applications (Spaces), and accessing the full Hub API. Supports remote HTTP-streaming via https://huggingface.co/mcp with OAuth or Bearer token auth, as well as local stdio deployment. Works with Claude, Gemini CLI, VS Code, Cursor, and any MCP-compatible client.

Official Apify MCP server that gives AI agents access to thousands of Actors for web scraping and automation. Agents can run Actors to extract data from websites, search engines, social platforms, and maps, then retrieve structured results from the dataset, enabling rich web research and data collection.

Official MCP server for interacting with MongoDB databases and MongoDB Atlas. Enables AI agents to query collections, run aggregations, manage indexes, inspect schemas, and perform CRUD operations. Also supports Atlas cloud management including cluster provisioning, database user management, performance advisor, and stream processing. Supports read-only mode for safe exploration.

Provides up-to-date, version-specific documentation and code examples for libraries, frameworks, and SDKs directly into your AI prompts. Instead of relying on potentially outdated training data, Context7 fetches current documentation from the source. Supports thousands of libraries including React, Next.js, Node.js, Python packages, and more.

Official MCP server for Qdrant vector search engine. Acts as a semantic memory layer enabling AI agents to store and retrieve information using vector similarity search. Supports storing text with metadata, semantic querying, configurable embedding models via FastEmbed, and both cloud-hosted and local Qdrant instances. Useful for building RAG pipelines, code search, knowledge bases, and long-term agent memory.