Popular MCP Servers

The MCP server ecosystem is large enough that most common integrations already exist. The filesystem server gives AI models read-write access to local directories. The GitHub server provides full repository access — reading files, creating issues, managing pull requests, searching code. The PostgreSQL and SQLite servers let models query databases directly. The Slack server enables reading and sending messages. These are the building blocks that cover 80% of use cases.

Browser automation servers are a category unto themselves. Puppeteer and Playwright servers give AI models the ability to navigate web pages, click buttons, fill forms, take screenshots, and extract content. This turns the AI into a web automation agent. Combined with other servers, you can build workflows like: read a GitHub issue, search the web for relevant documentation, navigate to the internal wiki, and update the issue with findings — all through MCP.

Enterprise-grade servers are emerging rapidly. Sentry provides error tracking and monitoring. Cloudflare exposes worker and DNS management. Docker enables container lifecycle management from within the AI conversation. Stripe offers payment and subscription tools. The pattern is clear — every major platform is shipping an MCP server because the demand from AI-powered development tools is undeniable. When evaluating a SaaS vendor in 2026, "do they have an MCP server?" is a legitimate selection criterion.

1. Developer Tools GitHub, GitLab, Linear, Jira — repository management, issue tracking, project management. These are the most commonly used MCP servers for development workflows.
2. Data & Databases PostgreSQL, SQLite, MongoDB, BigQuery — direct database querying from the AI conversation. Schema discovery, query execution, and result formatting handled by the server.
3. Communication Slack, Discord, Email — reading messages, sending notifications, managing channels. Enables AI assistants that participate in team communication.
4. Infrastructure Docker, Kubernetes, Cloudflare, AWS — container management, DNS, deployments. For DevOps workflows where the AI needs to inspect or modify infrastructure.
5. Browser Automation Puppeteer, Playwright, Chrome DevTools — web navigation, form filling, screenshot capture. For testing, scraping, and web-based workflow automation.