The Best MCP Servers of 2025

The year 2025 has been truly significant for AI as we saw a wave of incredible models and tools, each one faster and more capable than the last. One of the most significant things that has been released has been the model context protocol which was released by Anthropic back in late 2024 and it really blew up. A lot of products and services were being built around it. And as this year wraps up, I want to share the six best MCPs that truly changed the way that I fundamentally look at development now. But before that, a quick word from our sponsor, Blink. If you've ever tried building an AI powered SAS, you know the hardest part isn't the idea. It's picking models, managing credits, and getting everything to work together. Blink takes care of all that. With Blink, you don't just build apps fast. You get full control. Choose your own AI models like Claude Opus, Gemini, or GPT. Or let Blink's auto mode pick the best one for your use case, optimizing output and credit usage, unlike tools that quietly drain credits. Designing your app is just as simple. Upload screenshots from Figma or share inspirations from Pinterest or Dribble and Blink recreates the UI beautifully. Integrating AI is seamless, too. Whether chat, images, audio, or video, Blink guides you so everything works smoothly. If you want to build real polished apps that actually ship, Blink is your shortcut. Click the link in the pinned comment and start building today. Let's start with an MCP server that transformed the way I work with AI code editors. Context 7. Context 7 pulls all the up-to-date version specific documentation and code examples directly into the AI coding agent. This eliminates a lot of issues that arise during AI coding, such as mismatching of dependencies. Instead, it provides your AI agent with a knowledge base on how to use any library. It's available on multiple plans, including a free one that's limited to open- source libraries. To use it, you simply sign up, create an API key, and install it into your preferred coding tool using the install commands. Once that's done, the MCP and all its tools will be ready to use in your project right away. Using the MCP, the model can look up the documentation of the framework I ask it to use. It then makes tool calls to retrieve documentation and quick start guides, implementing the task using that documentation as a reference. Unlike simple web search which returns unstructured and often vague results, context 7 retrieves relevant documentation snippets because they maintain a vector database of documentation which is frequently updated and use semantic search to get data whenever any query is encountered. There is also another tool that works in a similar way called ref which is basically a context efficient version of context 7. It links together features like context 7 capabilities, web search, web scraping and code search on a single platform. Ref uses semantic search and unlike context 7 which injects large documents into the context window, it exposes only the relevant part to your specific question. But its free plan contains very limited credits after which you have to move to paid tiers. So unless you need those extra features, context 7 is the better choice. This next MCP is really important in terms of context saving and acting as a bridge between all the MCPs. The Docker MCP. It actually uses two tools to let you connect with many MCPs directly within your AI agent. One key feature of this MCP is reducing tools exposed in the context. Docker maintains a catalog of verified MCP servers that you can trust. You just need to add a single MCP server to the AI client you are using and connect the MCPs you need to access in Docker. Then when you connect to your client and ask it to use any connected MCP, it will use tools like MCP find and MCP add to access the MCP via Docker and return the results to you. By using Docker MCP, only the tools required for the specific query are loaded, which prevents the context from being bloated with unnecessary tools. So now your context window consists of only two tools, even if the MCPS you have connected in Docker contain hundreds. It's also highly secure because all the tools run in a sandbox within Docker. The fundamental problem faced while using MCP is a bloated context window due to many tools exposed in the context window while only a few are actually needed. Cloudflare and Anthropic both highlighted this and Cloudflare gave the general concept of the solution calling it code mode. Docker was actually the first one to fix this problem. We have previously made a video that demonstrates what code mode is and how it solves the problem. Code mode also allows dynamic MCP which enables AI agents to go beyond simply finding tools and create a JavaScript enabled tool that can call other MCP tools. We demonstrated this in our video showing how much time and context this feature actually saves. Now coming to my personal favorite and go-to MCP server for UI components, the Shadcen registry MCP server. Shad CNN is a really cool library of UI components that are fully customizable and you can use them directly in your web applications. But if you use them directly in your UI without it, you might encounter a lot of issues because the AI agent does not have specific context of the components. But with this MCP, everything changes. This MCP allows you to get the components directly and install them. Now, Shadzen MCP also lets you connect registries. A registry is basically an index that tells where to get particular components from and what their dependencies are to install them correctly. This MCP server allows you to interact with items from Shaden registries and get components from them like aceternity UI, magic UI, and many others. It's pretty simple to install. Just copy and paste the command and the MCP will be configured and ready to use right away. Adding custom registries is just as simple as adding a few lines of code to the components.json file. And honestly, I've used it a lot to build beautiful UI components. This is a fairly new one, but Google just announced a fully managed MCP server that gives you access to Google Cloud services. Launched alongside Gemini 3, this server introduces the Google Maps MCP. It allows agents to use location-based grounding, pulling accurate data directly from Google Maps and opens up new possibilities for your AI agents. The BigQuery MCP enables agents to interpret enterprise data while eliminating the issues of sensitive data in the context window. Additionally, they launched the Google Compute MCP which allows the MCP to manage cloud services and with the Kubernetes MCP container operations have never been this simple. All of these new MCPs are remote and they're also not open source. Their quick start guides are linked on their GitHub repo which I will link in the description below, but we can't go without mentioning the other Google services MCPs. These are open- source and include Google Workspace, Firebase, Google Analytics, Flutter, and many more. Out of all of them, I have used the Firebase MCP a lot in my projects. Since we run a YouTube channel and manage all our content, uploads, deadlines, research, and systems in Notion, the Notion MCP has been the most helpful for us. It's super easy to install. Just run a single command, and it's set up right away. You only need to authenticate it the first time you install it, and it comes equipped with all the tools needed to manage your Notion pages. Using this set of tools, it can search, fetch, create, update, move, and handle a wide range of tasks within your connected workspace. There are other amazing uses for the notion MCP as well. I personally use Claude and the Notion MCP to manage my team content states, track the ideas we have in the pipeline, and add new ideas or refine them. It has significantly helped me keep track of and simplify my day-to-day tasks and workflow using the Notion MCP. There is also an Obsidian MCP with similar capabilities just in case you don't use Notion for your tasks. The Obsidian MCP can do all of the same operations and manage your pages. Ending with one of the most powerful MCP servers that I honestly have started using in most of my projects, the Superbase MCP. Since we use Superbase for most of our backends in the smaller projects we ship, this MCP has been a tremendous help. It eliminates the need to manually write SQL queries or manage database schemas and configurations. With this MCP, your AI code editor can handle everything on its own from database schema management to SQL operations and you just have to direct it via prompting on the platform you're using. The installation process is pretty simple. You just need to log in to the MCP and authenticate it and all the tools will be available for use. After that, you simply ask your AI tool to create a proper database for you. It can handle everything from creating the project to managing costs and setting up the entire environment all by itself. That brings us to the end of this video. If you'd like to support the channel and help us keep making videos like this, you can do so by using the super thanks button below. As always, thank you for watching and I'll see you in the next one.