9 Best MCP Servers in 2026 (and What to Skip)

AI tools are good at writing and explaining, but they can’t see your tickets, your code, or your team chat unless you connect them. An MCP server is what makes that connection. It’s a small program that lets an AI tool work with an app you already use, like GitHub, Slack, or Jira.

There are many MCP servers now, and lots of lists still recommend ones that are out of date. This guide covers nine made by the companies behind the products, with setup details for each, plus a few to avoid and some basic safety rules. If MCP is new to you, start with our short explainer on how MCP works.

What are the best MCP servers? The short list

For most teams, GitHub plus the tool you use for tickets, chat, or notes covers the basics. The nine below are the ones worth knowing:

• GitHub: code, issues, and pull requests
• Atlassian: Jira and Confluence
• Slack: channels and conversations
• Notion: pages and databases
• MongoDB: your MongoDB data
• AWS: AWS documentation and your AWS account
• Cloudflare: your Cloudflare account and logs
• Playwright: testing websites in a real browser
• Context7: up-to-date library documentation

What is an MCP server, and why use one?

An MCP server is a small program that gives an AI tool access to another app’s data and actions. Without it, you’d copy and paste information between the app and the AI by hand. One server works with any AI tool that supports MCP, including Claude Code, Codex, and Cursor.

How did we choose these MCP servers?

We kept to servers that are made by the company behind the product, have current setup instructions, and let you limit what they can do. We left out anything we couldn’t confirm still works. Popularity alone wasn’t enough, because several popular servers have since been retired.

The 9 best MCP servers in 2026

Best MCP servers for team tools

These four cover where most teams keep their code, tasks, conversations, and notes.

1. GitHub MCP server

GitHub’s own server is probably the most useful one here for anyone who writes code. It lets an AI tool read repositories and code, manage issues and pull requests, check GitHub Actions runs, and look at security alerts. You can ask things like “Which pull requests are waiting for my review?” or “Why did last night’s build fail?” and get an answer from your real repository, without copying anything across.

It comes in two forms. One is hosted by GitHub and has been generally available since September 2025. The other you run yourself, as a Docker image or a program. It also works with GitHub Enterprise Server. GitHub’s repository includes setup guides for common AI tools, including Claude.

Two settings are worth knowing. Read-only mode offers only the tools that read, and a toolsets setting lets you turn on just the parts you need, such as repositories and issues. Using fewer tools also helps the AI choose the right one.

How to connect: Use the hosted address https://api.githubcopilot.com/mcp/. To turn on read-only mode, add the header X-MCP-Readonly: true. To limit it to certain parts, add X-MCP-Toolsets: repos,issues.

Watch for: Unless you use read-only mode, it can change your code, issues, and pull requests, so start there.

GitHub repository: https://github.com/github/github-mcp-server

2. Atlassian MCP server (for Jira and Confluence)

If your team plans work in Jira and keeps documents in Confluence, this is the official way to connect them. It’s hosted by Atlassian and connects an AI tool to Jira, Confluence, Jira Service Management, Bitbucket, and Compass. You can search and summarize content across them without switching between apps. It can also turn notes into Jira tickets, and create Confluence pages or Jira items in bulk.

Security is handled well. The AI only sees what the person using it is already allowed to see. Atlassian also says the server doesn’t store or cache your Jira or Confluence content. Every Atlassian Cloud customer can use it, though limits on usage depend on your plan.

How to connect: Use https://mcp.atlassian.com/v1/mcp and sign in with your Atlassian account in your browser. A site admin has to approve it the first time. It works with Atlassian Cloud only. You need a Cloud site.

Watch for: Atlassian says it doesn’t currently support FedRAMP or HIPAA requirements, which matters for regulated teams. If your company limits access by IP address, you’ll need to allow this server’s traffic.

GitHub repository: https://github.com/mcp/com.atlassian/atlassian-mcp-server

3. Slack MCP server

Much of a team’s thinking happens in Slack: decisions, questions, and half-finished ideas. This server lets an AI tool read that history. It covers messages, channels, users, and canvases, and helps with finding decisions, summarizing discussions, and following activity across channels. A good use is asking “What did we decide about the launch date last week?” and getting an answer from the real conversation.

Slack hosts the server itself and uses a sign-in step in your browser. After you sign in once, your AI tool reuses the login. Slack’s documentation has setup steps for Claude and Cursor, among others. Usage limits follow the same limits as Slack’s regular developer API.

How to connect: Use https://mcp.slack.com/mcp. In Claude Code, run claude mcp add --transport http slack https://mcp.slack.com/mcp.

Watch for: It can read your workspace, so check which channels it can see before you connect it.

GitHub repository: https://github.com/korotovsky/slack-mcp-server

4. Notion MCP server

Many teams keep meeting notes, plans, and wikis in Notion. This server lets an AI tool find and use them. It can search, read, create, and update pages, databases, views, and comments after you sign in. You could ask it to find last quarter’s planning page, summarize it, and add a new page with next steps.

Notion hosts the server at https://mcp.notion.com/mcp. Notion’s guide recommends this address over the older /sse one. In Claude Code, run claude mcp add --transport http notion https://mcp.notion.com/mcp.

Watch for: The server can change and delete content in your workspace, so be careful with what you ask it to update. Also, Notion’s older open-source version is no longer actively maintained, and Notion recommends the hosted one.

GitHub repository: https://github.com/makenotion/notion-mcp-server

Best MCP servers for databases and cloud accounts

These three connect an AI tool to your data and your cloud setup.

5. MongoDB MCP server

MongoDB’s official server lets you ask questions about your data in plain English instead of writing queries. It works with both MongoDB databases and MongoDB Atlas. It can find and summarize records, list your databases and collections, and read how a collection is laid out. The Atlas tools for managing clusters only appear if you give it your Atlas credentials, so you can keep it to the database alone.

It has several safety controls. Read-only mode blocks anything that writes. You can also switch off specific tools or groups of tools. A dry-run mode shows you which tools are on before anything runs.

How to connect: Run npx -y mongodb-mcp-server and give it your connection string.

Watch for: Writes are allowed by default, and MongoDB recommends always turning on read-only mode. Use --readOnly. If you want your AI answers grounded in your own data, see our RAG development work.

GitHub repository: https://github.com/mongodb-js/mongodb-mcp-server

6. AWS MCP servers

AWS offers two servers that do different jobs. The managed AWS MCP Server runs inside AWS and carries out actions in your account using the permissions of the AI tool that’s connected. That’s useful for checking your setup or making changes, as long as the permissions are set sensibly.

The AWS Knowledge MCP Server is the simpler one to try first. It’s free and managed by AWS, it gives an AI tool current AWS documentation and code samples, and you don’t need an AWS account to start. It also knows which AWS services and features are available in which regions. It’s a good fix for AI tools that give out-of-date AWS advice.

How to connect: The documentation server is at https://knowledge-mcp.global.api.aws. Check the AWS docs for the managed server, since it needs your AWS sign-in details set up.

Watch for: AWS also keeps an open-source collection of servers, and several of them are marked deprecated, including the CDK, Cloud Control API, and Terraform ones. Read the labels before installing. If your systems run on AWS, see our Cloud DevOps services.

GitHub repository: https://github.com/awslabs/mcp

7. Cloudflare MCP servers

Cloudflare runs a set of servers, each aimed at a different job. The main one manages your Cloudflare account. Others cover documentation, Workers bindings, logs and debugging, and analytics. For example, you could ask the logs server why an app is failing, or ask the analytics server how traffic changed this week.

When you connect, you’re sent to Cloudflare to sign in and choose which permissions the AI gets. That makes it easy to start narrow.

How to connect: Use https://mcp.cloudflare.com/mcp for the account server, or pick a specific one such as the documentation server at https://docs.mcp.cloudflare.com/mcp.

Watch for: Give only the permissions the task needs.

GitHub repository: https://github.com/cloudflare/mcp-server-cloudflare

Best MCP servers for building and testing software?

These two are useful if you use an AI tool for development work.

8. Playwright MCP server

Playwright is Microsoft’s tool for testing websites, and its server gives an AI tool a real browser to work with. Instead of looking at screenshots, the AI reads the structure of the page, which makes its actions more reliable. You can ask it to open your sign-up page, fill in the form, and tell you if anything breaks. It can also watch network traffic and fake responses from your own services.

The server starts even if no browser is installed yet, but you’ll need to install one before it can open pages.

How to connect: Run claude mcp add playwright -- npx @playwright/mcp@latest.

Watch for: One of its tools can run any Playwright code, so keep it to test sites.

GitHub repository: https://github.com/microsoft/playwright-mcp

9. Context7 MCP server

AI tools often write code for an old version of a library, because that’s what they learned from. Context7, made by Upstash, fixes this. It pulls current, version-specific documentation and code examples straight into your prompt. If you mention a version, it fetches the docs for that version. If you name the library with a short ID, such as /vercel/next.js, it skips the lookup step.

How to connect: Run claude mcp add context7 -- npx -y @upstash/context7-mcp@latest.

Watch for: The hosted version at https://mcp.context7.com/mcp needs an API key, so check the current setup steps.

GitHub repository: https://github.com/upstash/context7

Which MCP servers are out of date and best avoided?

Lists that still recommend these are out of date:

• The MCP project’s own servers page now keeps only seven examples (Everything, Fetch, Filesystem, Git, Memory, Sequential Thinking, and Time), and says they’re meant for learning, not production use.
• Thirteen older ones were archived, including GitHub, GitLab, PostgreSQL, SQLite, Slack, Sentry, Google Drive, Puppeteer, and Redis. Use the maker’s own server instead.
• Notion’s older open-source server is no longer actively maintained.

To browse more, the official MCP Registry lists published servers. It shows where a server comes from, but it doesn’t check the code.

How to use MCP servers safely?

An MCP server can do whatever you allow, and the AI decides when to use it. Text the AI reads, like a ticket or a web page, can be written to trick it into doing something else. This is called prompt injection. Atlassian warns about it in its own documentation and advises giving the least access possible.

A few habits cut most of the risk:

• Start in read-only mode wherever a server offers it.
• Give each connection access to one project or database, not everything.
• Use test data instead of live data.
• Keep your AI tool’s approval prompts turned on.
• Choose servers hosted by the maker, and read the code of any community server first.

Start with two or three servers. Each one adds instructions the AI has to read, so more isn’t better. If you’re setting this up across a company, our DevSecOps team can help you set access rules first.

How can NextGenSoft help you connect AI tools to your systems?

Right now, choosing the servers is the easy part. The harder part is deciding what each AI tool can read, what it can change, and who approves it. We help teams design that setup, build AI agents (AI tools that carry out tasks), and connect them to existing systems.

You can read more about our AI services and Contact us to fulfill your requirements.

Frequently asked questions about MCP servers

1. What are the best MCP servers to start with?
Answer: GitHub, plus the tool you use most for tickets, chat, or notes (Atlassian, Slack, or Notion). Add Playwright and Context7 if you use AI for development work.

2. Which MCP servers work with Claude Code?
Answer: All of them. A server works with any AI tool that supports MCP, including Claude Code, Codex, and Cursor. The entries above show the Claude Code command for Slack, Notion, Playwright, and Context7.

3. Are MCP servers free?
Answer: Most are open source. You may still pay for the service behind them, such as a Notion, Atlassian, or AWS account.

4. Are MCP servers safe to use?
Answer: They’re as safe as the access you give them. Use read-only mode, narrow permissions, and test data.

5. How many MCP servers should I use?
Answer: Two or three to begin with. Add another only when you need it.