8/11/2025

Finding the Best Open-Source MCP Servers for Your AI Agent Projects

What’s up, everyone? Let's talk about something that's been bubbling up in the AI dev world & is honestly starting to change the game for building serious AI agents. I’m talking about MCP servers.

Now, if you’ve heard the term, you might be a little confused. A quick search might show you stuff about Minecraft, & you'd be half right. For a while, "MCP" in the AI space did stand for "Minecraft Control Panel," which devs used to create sandboxed worlds for training agents. It’s a pretty cool niche, giving agents a 3D world with physics to learn tasks like chopping wood or not falling into lava. It’s a legit AI lab without the risk of breaking a thousand-dollar robot.

But here’s the thing: a new, MUCH bigger definition has taken over. Today, when we talk about MCP, we're talking about the Model Context Protocol. & THIS is what you need to know about.

So, What Exactly is the Model Context Protocol (MCP)?

Honestly, the best way to think about it is like USB-C for AI. That analogy gets thrown around a lot, but it’s spot on.

Before MCP, if you wanted your AI agent to talk to an external tool—say, pull data from a database, check a user's calendar, or interact with a specific API—you had to build a custom, one-off integration for every single one. If you had 10 agents & 10 tools, you were looking at a potential nightmare of 100 different integrations to build & maintain. It was brittle, time-consuming, & just didn't scale. Your super-smart AI was basically trapped, isolated from all the useful data & tools it needed to actually do things.

Anthropic, the folks behind the Claude AI, saw this mess & introduced the Model Context Protocol in late 2024 as an open standard to fix it.

MCP is an open protocol that creates a standardized way for AI models to connect with & use external data, tools, & services. Instead of a tangled web of custom integrations, you have one consistent protocol. A developer can expose their tool via an MCP server, & any MCP-compatible AI client (like an AI agent) can connect to it & use its capabilities.

Suddenly, your AI isn't just a brain in a jar. It can connect to multiple sources at once, discover new tools on the fly, & participate in complex, multi-step workflows. This is how we get from AI that knows things to AI that does things.

The industry adoption has been SUPER fast. Companies like Block, Replit, Microsoft, & Sourcegraph jumped on board almost immediately. OpenAI even added MCP support to their Agents SDK. It’s clear this is the direction things are heading.

Why MCP is a Game-Changer for AI Agent Projects

This isn't just about making a developer's life a little easier. The shift to a standardized protocol like MCP has some pretty profound implications:

Modularity & Flexibility: You can swap out models, tools, or even entire platforms without having to rewrite all your integrations. Want to switch from one LLM to another? No problem. As long as they both speak MCP, your tools will still work.
Enhanced Security: MCP is designed with security in mind. It allows you to expose data & tools in a controlled way, keeping your sensitive information within your own infrastructure while still letting the AI interact with it.
Scalability: The client-server architecture is built to scale. It supports various transport methods like stdio, WebSockets, & HTTP, so it can work for a local script on your machine or a massive, distributed enterprise system.
Democratization of Tools: Any developer or company can create an MCP server for their product. This has led to a rapidly growing ecosystem of open-source & official servers, giving you a massive library of pre-built integrations to plug your agent into.

Think about building a modern customer service solution. You need an AI that can handle conversations, but what happens when a user asks for their order status? Or wants to know the return policy for a specific product? Instead of hard-coding that logic, an AI agent can use MCP to connect to a database server for the order status & a file system server to pull the latest return policy document.

This is where a platform like Arsturn comes into the picture. You could build a custom AI chatbot with Arsturn, trained on your company's knowledge base. When a complex query comes in that requires accessing real-time data from another system, the underlying agent could use MCP to connect to the necessary server, get the information, & provide an instant, accurate answer to the customer, 24/7. It’s about creating seamless, intelligent experiences by connecting different systems, & MCP is the glue that holds it all together.

The Universe of Open-Source MCP Servers

Okay, so you're sold on the idea. Where do you find these magical servers that give your AI superpowers? The open-source community has been on fire, & there’s already a huge collection of servers for pretty much anything you can imagine.

The best place to start is the

awesome-mcp-servers

list on GitHub, but here's a breakdown of the major categories you'll find, with some standout examples.

1. Database & Data Platform Servers

This is one of the most critical categories. Giving your AI agent the ability to securely query databases is fundamental for any data-driven task.

Google Cloud's MCP Toolbox for Databases: This is a BIG one. It's an open-source MCP server that lets your agent connect to a ton of different databases, including PostgreSQL, MySQL, SQL Server, Spanner, Bigtable, & even third-party ones like Neo4j. It handles all the messy stuff like authentication (OAuth2, OIDC) & connection pooling for you.
GreptimeDB MCP Server: If you're working with time-series data, this server allows your AI assistant to interact with GreptimeDB to explore schemas & run SQL queries.
Milvus MCP Server: For AI applications doing similarity searches or working with vector embeddings, the Milvus server allows your agent to manage collections & perform vector searches using natural language.
WSO2 FHIR MCP Server: This is a fantastic example of a specialized data server. It’s built for healthcare applications & allows agents to interact with data using the Fast Healthcare Interoperability Resources (FHIR) standard. This means an AI can search for patient records, clinical data, or immunization histories in a standardized, secure way. It even supports the SMART-on-FHIR authentication protocol.

2. Code Execution & Developer Tool Servers

These servers turn your AI agent into a true development partner. They can read code, write code, manage infrastructure, & interact with dev tools.

Pulumi MCP Server: This is a game-changer for Infrastructure as Code (IaC). It allows an AI agent to use the Pulumi Automation API to preview, deploy, & manage your cloud infrastructure. Your agent could literally analyze your application code & then generate & deploy the necessary AWS infrastructure for it, all through a simple prompt.
Portainer MCP Server: For anyone working with Docker & Kubernetes, this server is a must-see. It lets an AI assistant connect to your Portainer instance to manage containers, pull logs, inspect networks, & automate Docker tasks. You could tell your agent, "Clear the build cache & restart the web server container," & it would just do it.
Kubernetes MCP Servers (e.g.,
1rohitg00/kubectl-mcp-server
,
1silenceper/mcp-k8s
): There are several of these, & they all aim to do one thing: let you manage your Kubernetes cluster with natural language. Instead of memorizing complex
1kubectl
commands, you can just tell your AI what you want to do.
Git MCP Server: A reference server that provides tools for reading, searching, & manipulating Git repositories. An agent could use this to review pull requests, search for code snippets across branches, or analyze repository history.

3. API Integration & Web Automation Servers

This category is all about connecting your agent to the wider world of the internet & third-party services.

OpenAPI-to-MCP: This is a super powerful concept. These servers can take any existing API that has an OpenAPI (formerly Swagger) specification & automatically turn it into an MCP server. This instantly makes a vast number of existing web services available to your AI agent without you having to write a single line of integration code.
Browser Automation Servers (e.g.,
1imprvhub/mcp-browser-agent
, Puppeteer): These servers give your agent control over a web browser. They can be used for web scraping, filling out forms, taking screenshots, or performing automated testing.
ScrapeGraph MCP Server: A more specialized web scraping tool that uses AI to extract structured data from webpages, making it much more powerful than simple HTML parsing.
Application-Specific Servers (Slack, GitHub, Google Drive, etc.): Many popular applications now have official or community-built MCP servers. These allow your agent to perform actions directly within those apps—like sending a message on Slack, creating an issue on GitHub, or searching for a file in Google Drive.

4. Aggregators & Management Servers

As the number of MCP servers grows, a new category has emerged to help manage the complexity.

1mcp/agent: This is a unified MCP server that can aggregate multiple other MCP servers into a single endpoint. This simplifies the client-side configuration immensely.
TheLunarCompany/lunar#mcpx: A production-ready, open-source gateway for managing MCP servers at scale. It helps you centralize things like tool discovery, access control, & usage tracking, which is essential for enterprise-grade agent workflows.

How to Choose the Right MCP Server for Your Project

Okay, that's a lot of options. So how do you pick the right one? Here's what I'd look for:

Functionality: This is the most obvious one. Does the server do what you need it to do? Does it connect to the right database, API, or tool?
Official vs. Community: Is the server an official implementation maintained by the company behind the tool (like the Pulumi or WSO2 servers), or is it a community project? Official ones might offer better long-term support, but some of the most innovative servers come from the community.
Security Features: How does the server handle authentication & authorization? For anything connecting to sensitive data, you want to see robust security practices, like support for OAuth, API keys, & configurable access controls. The Google Cloud database server is a great example of this.
Ease of Use & Documentation: How easy is it to get the server up & running? Is it available as a Docker container? Is the documentation clear? A project with good docs & a simple setup process will save you a ton of headaches.
Language & Environment: What is the server written in (Python, Go, TypeScript)? Does it fit well with your existing tech stack & deployment environment (e.g., Kubernetes-native)?

Building powerful, autonomous agents is ALL about connecting them to the right tools & data. Whether you're trying to automate business processes, streamline customer support, or create smarter developer tools, you'll need a way for your AI to interact with the world. This is where you can leverage a platform like Arsturn to build the conversational front-end for your business. Arsturn helps businesses create no-code AI chatbots trained on their own data, which can then be supercharged by connecting them to these powerful open-source MCP servers to perform real-world actions, boost conversions, & provide deeply personalized customer experiences.

What's Next?

The Model Context Protocol is still young, but it’s growing at an incredible pace. It represents a fundamental shift in how we build agentic AI systems—moving from siloed, custom-coded hacks to a more open, standardized, & interoperable ecosystem.

If you're a developer working on AI agents, now is the time to dive in. Pick a simple project. Maybe try setting up the Filesystem server to let an agent read a local text file, or connect to the Portainer server to list your running Docker containers. You'll quickly see how powerful this concept is.

The future of AI isn’t just about bigger models; it’s about better-connected models. & MCP is the standard that’s making those connections happen.

Hope this was helpful! Let me know what you think or if you've experimented with any cool MCP servers yourself.