MCP Servers
The open registry for Model Context Protocol servers. Find the right tools, resources, and prompts for your AI agents — filtered by category, transport, or use case.
Servers
206
Tools
545
Categories
11
Contributors
182
Official Expo MCP server that connects AI coding assistants to Expo projects and EAS services. Enables searching and reading Expo documentation, managing EAS builds and workflows, installing compatible libraries, inspecting TestFlight crashes and feedback, and automating visual verification through simulator screenshots and interactions. Supports both remote server capabilities and local development server features for advanced automation.
GitHub's official MCP Server that connects AI tools directly to GitHub's platform. Enables AI agents to manage repositories, issues, pull requests, branches, files, actions workflows, and code security. Supports both remote (OAuth) and local (Docker/binary) modes with fine-grained toolset configuration.
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 GitLab MCP server connecting AI tools to GitLab's DevOps platform. Enables agents to manage projects, issues, merge requests, branches, files, CI/CD pipelines, and the GitLab Duo workflow. Supports both GitLab.com SaaS and self-managed instances with fine-grained access tokens.
Official SonarQube MCP server that brings code quality and security analysis into AI workflows. Lets agents fetch project issues, security hotspots, quality-gate status, and metrics from SonarQube Server or SonarCloud, so code health can be inspected and triaged conversationally.
Official Plaid MCP server for financial data connectivity. Lets AI agents work with Plaid's APIs to retrieve accounts, balances, and transactions, and to build and debug integrations for banking, payments, and identity. Useful for fintech development, personal finance tooling, and automating account data workflows.
Contentful's official MCP server for the Content Management API. Gives AI agents tools to create, edit, organize, and publish content, manage content models and assets, and work across spaces and environments. Useful for automating editorial workflows and content operations in a Contentful-backed headless CMS.
Directus' MCP integration connects agents to headless CMS data, schemas, and operations for governed content and application workflows.
E2B's MCP server gives AI agents the ability to run arbitrary code in secure, isolated cloud sandboxes. Each sandbox is a fast-booting micro-VM where models can execute Python and shell commands, install packages, read and write files, and capture stdout/stderr — ideal for code interpretation, data analysis, and agentic workflows that need real execution.
Mapbox's official Model Context Protocol server, giving AI agents access to Mapbox's geospatial APIs. Supports forward and reverse geocoding, routing and directions, isochrone and matrix travel-time computation, and static map image generation, so assistants can answer location questions and build map-driven workflows from natural language.
MCP server that bridges AI assistants with the Unity Editor. Gives an LLM tools to create and modify GameObjects, edit scripts, manage assets and scenes, read the console, and run tests, enabling AI-driven game development workflows directly inside Unity. Works with clients such as Claude, Cursor, VS Code, and other MCP-compatible tools.
Medusa's MCP integration connects agents to commerce data and administrative operations for storefront, catalog, order, and customer workflows.
Payload CMS provides an MCP integration for agents to explore and manage CMS content, schemas, and application workflows.
Postman's official MCP server that connects the Postman platform to AI tools. Gives agents the ability to access workspaces, manage collections and environments, work with API specifications, run requests, and automate API workflows through natural language. Useful for exploring, testing, and maintaining APIs directly from an MCP-compatible assistant.
Skills vs MCP servers
what’s the difference?Skillsthe “what to do”
A skill packages know-how — instructions, an example prompt, and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP server gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transport. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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