Top 10 MCP Servers Every Developer Should Know

MCP servers extend AI with real-world capabilities. For developers, the right servers turn AI into a genuine development partner. Here are the 10 MCP servers that every developer should have in their toolkit.

1. File System Server

What it does: Gives AI the ability to read, write, and manage files on your local machine.

Why you need it: AI can read your entire project, make code changes, and create new files — all within the context of your actual codebase.

2. Git Server

What it does: Provides AI with git commands — viewing diffs, checking history, managing branches.

Why you need it: AI can understand your version control history, help with merge conflicts, and generate meaningful commit messages based on actual changes.

3. Database Server (PostgreSQL/MySQL/SQLite)

What it does: Connects AI directly to your database. It can query data, inspect schemas, and help with migrations.

Why you need it: No more copying and pasting SQL results into chat. AI can query your database directly and analyze the results.

4. Web Search / Fetch Server

What it does: Lets AI search the web and fetch content from URLs.

Why you need it: AI can look up documentation, check Stack Overflow, research libraries, and verify information in real time.

5. Docker Server

What it does: Gives AI control over Docker containers — listing, starting, stopping, and inspecting them.

Why you need it: Debugging containerized applications becomes conversational. AI can check logs, inspect container state, and suggest fixes.

6. Puppeteer / Browser Automation Server

What it does: Lets AI control a headless browser — navigating pages, clicking buttons, filling forms, taking screenshots.

Why you need it: Automated testing, web scraping, and debugging front-end issues without leaving your AI workflow.

7. GitHub Server

What it does: Connects AI to the GitHub API — issues, pull requests, actions, repository management.

Why you need it: AI can review PRs, create issues, check CI/CD status, and manage your GitHub workflow.

8. Slack / Communication Server

What it does: Lets AI read and send messages in Slack (or similar platforms).

Why you need it: AI can monitor channels for relevant discussions, summarize threads, and help you draft responses.

9. AWS / Cloud Infrastructure Server

What it does: Gives AI access to cloud resources — S3, Lambda, EC2, and more.

Why you need it: Infrastructure management through conversation. AI can check resource status, deploy changes, and troubleshoot issues.

10. Memory / Knowledge Base Server

What it does: Provides AI with persistent memory across sessions.

Why you need it: AI remembers your project context, preferences, and past decisions. No more re-explaining your architecture every conversation.

Where to Find These

All of these and many more are available in the MCP server catalog on a-gnt. Each listing includes installation instructions, configuration details, and usage examples.

Building Your Stack

Start with 2-3 servers that match your workflow. Add more as you get comfortable. The goal is to build an AI development environment that's uniquely tailored to how you work.

The developers who adopt MCP servers early are going to have a significant productivity advantage. Don't sleep on this.