15 Official MCP Servers Worth Using in 2026. From Clicking to Delegating

Official tools from GitHub, Microsoft, Google, AWS, and other established technology companies

Developers used to switch between an IDE, a browser, issue trackers, monitoring dashboards, and cloud consoles to complete a single task. Those interfaces still matter, but they are no longer the only way to work.

With Model Context Protocol, or MCP, an AI coding agent can use tools exposed by connected services. It can retrieve repository context, inspect an error, test a page, or query infrastructure within the permissions you grant.

The dashboard is becoming optional for some workflows. The controls and safeguards behind it are not.

Here are 15 MCP servers published by the companies behind the platforms, not unofficial copies or random GitHub projects. Each has a concrete reason to belong in a developer workflow.

Code, Browsers, and Debugging

1. GitHub MCP Server

Access repositories, pull requests, issues, and GitHub Actions. Useful when a coding agent needs project history or CI results instead of guessing from the current file.

Ask your agent:

“Investigate a failed workflow and identify which pull request introduced the failure.”

Official GitHub MCP Server

2. Playwright MCP by Microsoft

Let an agent navigate pages, fill forms, and verify browser behavior through structured accessibility snapshots. This is a practical way to check generated frontend code.

Ask your agent:

“Test the registration flow locally and report the first reproducible failure.”

Official Playwright MCP

3. Chrome DevTools MCP by Google

Inspect console errors, network requests, and performance traces in a running Chrome browser. It complements Playwright by providing deeper diagnostics.

Ask your agent:

“Find the slowest network requests and investigate the main rendering bottleneck.”

Official Chrome DevTools MCP

4. Figma MCP

Give an agent structured design context, including layouts, components, and variables, rather than relying on screenshots alone.

Ask your agent:

“Implement a selected design using components that already exist in the codebase.”

Official Figma MCP documentation

5. Sentry MCP

Bring application issues and debugging context into your coding environment. An agent can investigate real failures and suggest what code to inspect next. Sentry currently labels this integration Beta.

Ask your agent:

“Summarize the most frequent unresolved error and propose a reproducible test.”

Official Sentry MCP

Databases and Team Context

6. Supabase MCP

Explore PostgreSQL schemas, migrations, and logs. Supabase also offers project scoping and a read-only option, which are worth enabling for investigation tasks.

Ask your agent:

“Review the database schema and suggest indexes without modifying data.”

Official Supabase MCP documentation

7. MCP Toolbox for Databases by Google

Connect agents to databases such as PostgreSQL, MySQL, and BigQuery, or define controlled database tools for recurring queries.

Ask your agent:

“Explain the tables and relationships needed for a reporting query.”

Official Google MCP Toolbox

8. Atlassian Rovo MCP

Retrieve context from Jira, Confluence, Bitbucket, and other supported Atlassian products. This helps an agent connect an implementation task to its requirements.

Ask your agent:

“Read a Jira issue, find related documentation, and draft an implementation plan.”

Official Atlassian Rovo MCP documentation

9. Linear MCP

Search and update issues, projects, and comments, subject to your permissions. Linear also provides a dedicated read-only endpoint.

Ask your agent:

“Group open bugs by component and identify missing reproduction steps.”

Official Linear MCP documentation

10. Notion MCP

Use project specifications and technical notes as context for agent tasks. The integration respects existing workspace permissions.

Ask your agent:

“Compare the current API implementation with the requirements in a Notion specification.”

Official Notion MCP documentation

Infrastructure and Deployments

11. Terraform MCP by HashiCorp

Retrieve current provider documentation, registry modules, and supported HCP Terraform workspace information. This reduces reliance on outdated infrastructure examples.

Ask your agent:

“Find the correct provider resources before drafting Terraform for a development VM.”

Official Terraform MCP documentation

12. Vercel MCP

Inspect projects, deployment status, and logs directly from a coding assistant. Useful when a build works locally but fails after deployment. Vercel currently labels its MCP server Beta.

Ask your agent:

“Explain the latest failed deployment using its build logs.”

Official Vercel MCP documentation

13. Cloudflare API MCP

Access Cloudflare services, including DNS, Workers, and R2, through authorized API operations. Its compact tool discovery approach avoids exposing thousands of API schemas at once.

Ask your agent:

“Review a DNS configuration and recommend changes without applying them.”

Official Cloudflare MCP documentation

14. AWS MCP Server

AWS provides a managed MCP server for current documentation and authorized AWS API operations. IAM policies control what the agent can do, while AWS auditing helps track its actions.

Ask your agent:

“Inventory development EC2 instances and summarize their configurations without changing resources.”

Official AWS MCP documentation

15. Google Compute Engine MCP

Connect agents to Google Cloud virtual machines, disks, snapshots, and related resources using Google Cloud permissions.

Ask your agent:

“List development VMs by project and zone, then flag resources that need a separate utilization review.”

Official Google Compute Engine MCP documentation

Which MCP Servers Should You Connect First?

You do not need all 15. Select a small set around the work you actually do, then expand only when a new task requires it.

For frontend work, start with GitHub, Playwright, Chrome DevTools, and Figma. Backend developers might prefer GitHub, Sentry, and either Supabase or Google’s database toolbox. For DevOps, GitHub, Terraform, and the relevant cloud provider are a focused starting point.

Five Checks Before Giving an Agent Access

Verify the publisher. Use official documentation and repositories to avoid lookalike packages.

Minimize permissions. Prefer read-only access for diagnostics and limit credentials to the relevant project.

Keep approvals for changes. Production writes, deletions, and infrastructure modifications should require explicit authorization.

Restrict exposed tools. More tools can increase context overhead and create additional security risks.

Audit what happened. Record the action, target resource, initiating identity, and outcome without leaking secrets.

The OWASP MCP Security Cheat Sheet offers a deeper technical reference.

From Clicking to Delegating

For years, software interfaces have been designed around human interaction. We navigate menus, open dashboards, configure settings, and click buttons to get things done.

MCP introduces another way of working. Instead of navigating multiple applications, we can describe what we want to accomplish and let an AI agent interact with the necessary tools.

This changes more than the interface. It changes how software needs to be designed. APIs must expose clear capabilities, permissions must be carefully scoped, and operations need to remain predictable, observable, and safe.

The challenge is no longer just making software easy for humans to use. It is also making it understandable and accessible to AI agents without losing control.

We are applying the same principle at Idlefy. Our cloud cost optimization platform lets developers access dev and staging VMs through Slack, Telegram, or the web, while lease-based automation shuts them down when they are no longer needed. With MCP integration, these infrastructure workflows can also become accessible to AI agents.

The web interface is not disappearing. It is becoming one of several ways to interact with software.