50 Best MCP Servers in 2026: The Definitive Directory

Why We Built This Directory

The MCP ecosystem has exploded. In November 2024, Anthropic released the Model Context Protocol spec and a handful of reference servers. By March 2026, there are over 12,000 MCP servers scattered across GitHub, npm, PyPI, and dedicated registries like Smithery, Glama, and PulseMCP.

The problem is not finding MCP servers. The problem is finding the right ones. For every polished, production-ready server with thousands of stars, there are dozens of abandoned weekend projects that will break on first use. You need a filter.

That is why we built the MCP directory at PopularAiTools.ai. We track 6,900+ verified MCP servers -- more than any other single directory. Each one is categorized, described, and linked to its source repository. We monitor stars, update frequency, and community adoption.

This article distills that entire directory into the 50 best MCP servers across 10 categories. Whether you are a developer connecting your AI coding assistant to databases and cloud services, a product manager automating workflows through Slack and Notion, or just getting started with MCP -- this is the list you need.

If you are new to MCP entirely, start with our complete guide to what MCP servers are and then come back here to pick your stack.

How We Ranked These Servers

Every server on this list meets four criteria:

Let us get into the categories.

1. Database MCP Servers

Database MCP servers let your AI assistant query, modify, and manage databases through natural language. Instead of writing SQL by hand, you describe what you need and the AI generates and executes the query directly. These are among the most immediately useful MCP servers you can install.

Our pick: Supabase MCP Server. It goes far beyond basic database queries. You get full platform control -- schema migrations, row-level security policies, edge function deployment, auth management, and storage. If you are building on Supabase, this single server replaces a dozen manual workflows. Read our full Supabase MCP Server review for setup instructions and a deep dive into every tool.

Runner-up: PostgreSQL MCP Server. The reference implementation from Anthropic's official MCP repository. Simple, reliable, read-only by default. Perfect if you just need your AI to understand your database schema and run SELECT queries without risk of accidental mutations.

2. Browser and Web MCP Servers

Browser MCP servers give your AI the ability to navigate websites, take screenshots, fill forms, scrape content, and interact with web applications. This is one of the most transformative categories -- it turns your AI from a text-only tool into something that can actually see and use the web.

Our pick: Playwright MCP Server. This is the gold standard. It is official (maintained by Microsoft), supports all three browser engines, and the accessibility-tree based snapshot approach means the AI understands page structure without needing screenshots. We use it daily for everything from automated testing to content screenshots. Our Playwright MCP Server review covers the full tool list and real-world usage patterns.

Runner-up: Firecrawl. If your primary use case is web scraping rather than interactive browsing, Firecrawl is purpose-built for it. It handles JavaScript-rendered pages, converts content to clean markdown, and supports batch crawling of entire sites.

3. Cloud and DevOps MCP Servers

Cloud MCP servers connect your AI directly to infrastructure. Deploy functions, manage DNS, provision databases, check logs, and configure CI/CD -- all through conversation. These servers turn your AI assistant into a capable DevOps copilot.

Our pick: AWS MCP Server. Amazon went all-in on MCP support. The official AWS MCP server covers S3, Lambda, DynamoDB, CloudWatch, ECS, and more -- with proper IAM scoping so you can limit exactly what your AI can touch. We covered the full toolset in our AWS MCP Server review.

Runner-up: Docker MCP Server. Docker's official MCP integration is exceptionally well done. It lets your AI manage containers, build images, inspect logs, and orchestrate Compose stacks. If you develop locally with Docker, this is a must-install.

4. Git and Code MCP Servers

Version control and project management servers connect your AI to the tools developers live in every day. Create issues, review pull requests, manage sprints, and triage bugs -- all without leaving your AI conversation.

Our pick: GitHub MCP Server. This is the most popular MCP server in the entire ecosystem, and for good reason. 51 tools, official first-party support, OAuth remote connection, and a team of GitHub engineers maintaining it. If you only install one MCP server, make it this one. Read our complete GitHub MCP Server review for the full breakdown.

Runner-up: Sentry MCP Server. An underrated powerhouse. Instead of manually searching through error dashboards, your AI can pull up stack traces, identify error patterns, and even suggest fixes based on the crash context. It turns error triage from a chore into a conversation.

5. Communication MCP Servers

Communication servers connect your AI to messaging platforms and email. Read messages, send notifications, search conversation history, and automate workflows across the tools your team already uses.

Our pick: Slack MCP Server. Slack is where most development teams live. This server lets your AI read channel history, search conversations, send messages, and even manage threads. The workflow possibilities are enormous -- automated standup summaries, incident response notifications, cross-channel search. It is one of those servers that saves minutes every single day.

Runner-up: Gmail MCP Server. Email is still the backbone of professional communication. Having your AI draft responses, search for specific threads, and triage your inbox saves a surprising amount of time. The OAuth setup is clean and Google-approved.

6. AI and ML MCP Servers

Meta, but powerful. AI/ML MCP servers let your primary AI assistant call other AI models and ML platforms. Generate images, run inference, search model hubs, and orchestrate multi-model workflows.

Our pick: Hugging Face MCP Server. The Hugging Face Hub is the largest open-source ML repository on the planet. This server lets your AI search through 800,000+ models, browse datasets, read papers, run inference, and even spin up Spaces. It is the gateway to the entire open-source AI ecosystem.

Runner-up: Ollama MCP Server. If you prefer running models locally for privacy or cost reasons, Ollama's MCP server is the bridge. Your primary AI can delegate specific tasks to local models -- summarization, embedding generation, code review -- without any data leaving your machine.

7. Productivity MCP Servers

Productivity servers connect your AI to the apps where you organize knowledge, manage projects, and track tasks. Search your notes, update your databases, manage your to-dos, and navigate your files -- all through natural conversation.

Our pick: Notion MCP Server. Notion has become the default knowledge base for startups and dev teams. This server lets your AI search across all your pages and databases, create new entries, and update existing ones. The killer use case is having your AI reference your own documentation while coding -- it can look up your internal API docs, design decisions, and meeting notes in real time.

Runner-up: Google Drive MCP Server. If your organization lives in Google Workspace, this server is essential. Your AI can search through Docs, read Sheets data, and find files across your entire Drive. It turns your file system into a queryable knowledge base.

8. Design MCP Servers

Design servers bridge the gap between code and visuals. Extract design tokens from Figma, generate diagrams, capture screenshots, and create visual assets -- all from your development workflow.

Our pick: Figma MCP Server. The design-to-code gap has plagued frontend development for years. Figma's MCP server lets your AI read design files directly -- extracting colors, spacing, typography, component structure, and layout constraints. Instead of eyeballing a mockup and guessing the padding, your AI reads the exact Figma values and generates pixel-perfect code. Our Figma MCP Server review walks through the full workflow.

Runner-up: Excalidraw MCP Server. Need a quick architecture diagram or flowchart? Describe it in natural language and get a hand-drawn-style diagram instantly. It is perfect for documentation, README files, and technical blog posts.

9. Data and Analytics MCP Servers

Analytics servers connect your AI to data warehouses, search engines, monitoring platforms, and product analytics tools. Query terabytes of data, build dashboards, investigate incidents, and analyze user behavior -- all through natural language.

Our pick: Grafana MCP Server. Grafana is the observability standard for infrastructure teams. This MCP server lets your AI query dashboards, investigate alerts, and correlate metrics across data sources. When an incident fires at 2 AM, having your AI pull up the relevant dashboards and identify anomalies cuts your mean time to resolution dramatically.

Runner-up: BigQuery MCP Server. If you have data in BigQuery, this server is transformative. Describe what you want to know in plain English, and your AI writes and executes the SQL query on your behalf. No more wrestling with complex JOIN syntax or window functions -- just ask the question and get the answer.

10. Utility MCP Servers

Utility servers provide foundational capabilities that other servers build on. File access, persistent memory, structured reasoning, HTTP fetching, and time awareness. These are the building blocks that make AI agents actually useful in the real world.

Our pick: Memory MCP Server. AI models forget everything between conversations. The Memory server solves this by maintaining a persistent knowledge graph -- entities, relationships, and observations that carry across sessions. This is the foundation for AI agents that actually learn and remember context about your projects, preferences, and workflows. Once you use it, you will never go back to stateless conversations.

Runner-up: Sequential Thinking. This one is subtle but powerful. It gives your AI a structured framework for breaking down complex problems into steps, with the ability to branch, revise, and track hypotheses. It is not a tool in the traditional sense -- it is a thinking scaffold. For multi-step reasoning tasks, architecture decisions, and debugging sessions, it measurably improves output quality.

How to Install Any MCP Server

Every MCP server on this list can be installed in under two minutes. The process is the same regardless of which server you choose:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_your_token_here"
      }
    }
  }
}

For detailed setup instructions for each specific server, click through to its detail page in our directory. Every listing includes the exact config block, required environment variables, and compatibility notes.

Frequently Asked Questions