I’ll admit it, I felt like a total moron when I first stumbled onto the term MCP server.

I kept seeing developers casually mention “MCP this” and “MCP that” in Discord and Reddit threads. One guy even said, “You’re not serious about AI unless you’re running a local MCP.”

Uh, what?

That moment of confusion sparked this post. And I’m glad it did, because it turns out Model Context Protocol (MCP) servers are the key to getting AI agents like ChatGPT, Claude, or Cursor to do actual work like run tests, fix performance issues, create files, or even deploy things.

So, why this blog exists

This post was inspired by a comment on Reddit:

“I don’t really understand the value of any of these tools. Are they proven in any way whatsoever? Who’s using them?” — u/SlimeQ

It’s a valid question. The short answer is: yes, devs are starting to use MCPs to power real workflows.

“I use a GitLab MCP to interact with our issues and open MRs. I’ll ask to look for related issues when I edit code to maybe drag it into scope, or just leave a note.” — SFauconnier
So WTF Is an MCP Server?

The term MCP server refers to any backend service that implements the Model Context Protocol — a spec that allows AI agents to interact with real-world tools via HTTP APIs.

They’re not browser extensions or ChatGPT plugins. They’re actual standalone servers that listen for requests from the AI and then do things like:

  • Modify files
  • Execute terminal commands
  • Fetch GitHub issues
  • Call APIs
  • Read/write to databases
  • Run test suites
  • Access APM insights
  • And a whole lot more

They act as a bridge between the LLM and the real world. And if you’re building your own SaaS app? You should seriously consider making it API-first, so future agents can control it via MCP. So, I decided to put together a blog on the most useful MCP servers, what they do, when to use them, and how they boost AI-assisted dev workflows. Of course, I included ours too.

Below is a curated list of popular MCP servers and tools that devs are actually using, with context on what they do and why you might want to try them.

1. Sequential Thinking MCP

Sequential Thinking MCP helps large language models break complex tasks into smaller, logical steps. It is especially useful for multi-phase planning like architectural design, system decomposition, or large-scale refactors. Think of it as your AI’s ability to think like a senior engineer, methodical, structured, and goal oriented.

2. Puppeteer MCP

Puppeteer MCP equips your AI with browser automation powers. It leverages Google’s Puppeteer library to simulate user interactions, test UI workflows, scrape data, or automate form submissions. Perfect for agents that need to interact with real websites or perform front end validations.

3. Memory Bank MCP

Memory Bank MCP serves as a centralized memory system for AI agents. It allows them to recall information across sessions and navigate large codebases with consistent context. Ideal for keeping track of multi file relationships, prior decisions, and project level understanding.

4. Playwright MCP

Playwright MCP uses Microsoft’s Playwright library to provide robust, cross browser automation. It is similar to Puppeteer but with more modern APIs and better support for testing across Chromium, Firefox, and WebKit. A go to choice for intelligent UI test automation or complex scraping.

5. GitHub MCP

GitHub MCP connects AI to GitHub’s REST API, allowing it to read issues, write comments, manage PRs, and trigger CI workflows. It acts as a bridge between AI and your version control system, ideal for automating reviews, syncing tasks, or pushing code with minimal human interaction.

6. Knowledge Graph Memory MCP

This MCP creates a persistent, graph based memory system for AI. It stores entities, their relationships, and context in a structured format. Excellent for navigating large, evolving codebases where understanding how pieces connect is just as important as the code itself.

7. DuckDuckGo MCP

DuckDuckGo MCP enables your AI to fetch real time information via search, no API key required. It is lightweight, fast, and ideal for resolving error messages, finding documentation, or exploring concepts while coding.

8. MCP Compass

MCP Compass acts like a discovery engine or package manager for MCPs. Ask your AI what it needs to accomplish, and Compass will recommend the right MCP tools to load. Think of it as the AI equivalent of npm or pip, but task aware.

9. Desktop Commander MCP

This MCP provides AI with safe, local terminal access, including file browsing, shell command execution, and log inspection. It turns your local machine into an extension of your AI, letting it act on recommendations immediately and securely.

10. Serena MCP

Serena is a smart, context aware refactoring engine that works with your AI to handle multi step code changes. It supports things like function extraction, module migration, and performance tuning, all based on how your project is structured.

11. Supabase MCP

This MCP allows AI agents to directly query and manipulate Supabase databases. It is useful for tasks like writing SQL, exploring schemas, or managing user records, especially in modern full stack and serverless development environments.

12. Digma MCP Server

Digma MCP Server taps into your runtime observability data and makes it available to AI. It enables smarter decisions during code reviews and refactors by exposing performance issues, test flakiness, and bottlenecks, all based on real usage patterns and telemetry data. It is ideal for teams who already use APM and want to make that data actionable during development.

13. OpenAgents

OpenAgents is a modular AI orchestration framework that supports multiple MCPs. It lets you compose intelligent agents capable of running terminal commands, automating browser tasks, remembering long term context, and more, all coordinated by natural language goals.

14. Continue

Continue is a developer first IDE plugin that brings AI completions, refactoring, test generation, and more into VSCode and JetBrains. It also integrates with MCPs, letting your AI perform tasks like running terminal commands or managing project memory from inside your IDE.

15. GPT Pilot

GPT Pilot is a full stack AI pair programmer that builds production ready apps end to end. From scaffolding the architecture to writing, testing, and refining code, GPT Pilot requires minimal input. It is one of the most autonomous co pilot tools available today and ideal for generating complete MVPs or bootstrapping new services quickly.

Wrapping Up: Best MCP servers

The Model Context Protocol is quietly becoming a standard for giving AI agents real-world superpowers. With the right MCP server, your assistant can:

  • Modify files
  • Run actual code
  • Query live databases
  • Pull from real observability data
  • Automate your dev workflows

This isn’t just hypothetical anymore, it’s already happening in terminals and IDEs all over Reddit and Discord. If you’re experimenting with agents or want to future-proof your toolchain, spinning up an MCP server is the best way to bridge the gap between AI and your stack.

Next, I’ll dive into the best AI coding IDEs and how they stack up for real development workflows — from quick refactors to building full features. Stay tuned.

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The Digma MCP Server leverages the data in your APM dashboards to assist the AI agent during code reviews, code and test generation, fix suggestions, etc., and enables the agent to drive performance improvements and cost reduction.

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