Imagine opening your IDE and asking, “Why did my last deployment fail?” and your assistant instantly pulls details from Vercel, checks Sentry for recent errors, and reviews your last commit on GitHub, all without you leaving your editor.
That’s what Model Context Protocol (MCP) is enabling. It gives developer tools and AI systems a shared language so they can share context in real time. Instead of every integration being its own isolated setup, MCP makes your tools part of the same conversation.
In this blog, we’ll look at 10 awesome MCP servers and clients shaping the developer experience in 2025.
Difference between an MCP Server and an MCP Client?
If you’re just starting to explore the Model Context Protocol (MCP) ecosystem, it helps to understand the two main building blocks: MCP servers and MCP clients. They work together, but their roles are very different.
What is an MCP Server?
An MCP server is where all the actual capabilities live. It connects tools, APIs, and data so your AI assistant can access them in a structured way. Think of it as the part that does the heavy lifting.
For example, a GitHub MCP server can let your AI fetch repo data, review pull requests, or even open new issues. A Sentry MCP server can surface recent errors, and a Stripe MCP server can pull transaction info when you’re debugging a payments flow. Basically, if it has data or an API, there can be an MCP server for it.
What is an MCP Client?
The MCP client is the side you actually interact with. It’s the interface where your AI assistant lives, tools like VS Code, Cursor IDE, or Gemini CLI. The client talks to different MCP servers and turns that data into something useful while you’re coding.
So instead of jumping between dashboards, your MCP client can pull logs from Sentry, check commits from GitHub, and push code to Appwrite, all without leaving your editor.
In short
- MCP servers provide the capabilities
- MCP clients use them to get the work done
10 best MCP servers
1. Appwrite MCP server
Appwrite’s MCP server lets your tools talk directly to your Appwrite project. It can create users, manage databases, handle storage, and more, all through simple prompts. It’s a clean way to connect your AI environment to your backend without extra setup.
Link: Appwrite MCP server
2. GitHub MCP server
Tired of hopping between tabs to check commits or review pull requests? The GitHub MCP server lets your AI assistant handle it. It reads repos, opens issues, manages PRs, and even inspects CI pipelines. Everything GitHub-related, inside your coding flow.
Link: GitHub MCP server
3. Sentry MCP server
The Sentry MCP server links your debugging data to your AI client. You can look up issues, search logs, analyze releases, or even ask for automated fixes. It gives you visibility into what’s breaking without digging through the Sentry dashboard.
Link: Sentry MCP server
4. Figma MCP server
Figma’s MCP server brings design context into your development workflow. It lets your tools pull components, variables, and layout details directly from a Figma file so your code reflects the real design instead of relying on screenshots or exports.
Link: Figma MCP server
5. MongoDB MCP server
The MongoDB MCP server turns your database into something you can actually talk to. Ask for schema details, run queries, or manage users, all in plain English. It’s a quick way to explore or modify data without opening the shell or console.
Link: MongoDB MCP server
6. Apify MCP server
Think of the Apify MCP server as a gateway to automation. It gives your assistant access to thousands of pre-built Actors for scraping, crawling, and data extraction. Need to collect product reviews or monitor prices? Your AI can spin up the right Actor instantly.
Link: Apify MCP server
7. Stripe MCP server
Stripe’s MCP server gives your AI client access to your billing and payment data. It can create customers, invoices, and subscriptions or look up transactions and balances. Everything runs securely through OAuth, keeping your financial operations simple and contained.
Link: Stripe MCP server
8. Vercel MCP server
If you deploy on Vercel, this can be helpful. The Vercel MCP server lets your assistant view build logs, check recent deployments, or roll back a release with a single prompt. It keeps the deployment context close to your code.
Link: Vercel MCP server
9. Sequential Thinking MCP server
This server helps AI tools reason through complex tasks. It can break a problem into smaller steps, explore different options, and refine answers along the way. It’s built for situations where careful, step-by-step thinking leads to better results.
Link: Sequential Thinking MCP server
10. Neon MCP server
Neon’s MCP server connects your AI client to your Postgres databases. It can create projects, run queries, manage branches, and handle migrations through natural language. It’s a straightforward way to work with Neon without switching between tools.
Link: Neon MCP server
9 best MCP clients
1. VS Code
VS Code remains one of the most widely adopted MCP clients. Its native integration allows developers to connect directly to multiple MCP servers within their workspace, enabling AI-assisted coding, debugging, and project management without leaving the editor.
Link: VS Code
2. Gemini CLI
Gemini CLI extends Google’s AI ecosystem to the terminal. As an MCP client, it can interface with servers like Appwrite, Stripe, and GitHub to generate code, query APIs, and manage deployments through simple commands.
Link: Gemini CLI
3. Zed
Zed combines low-latency performance with an integrated MCP layer. It provides an AI-assisted editing experience that supports real-time context exchange between code and connected tools, without the overhead of heavier IDEs.
Link: Zed
4. Claude Desktop
Claude Desktop supports connections to multiple MCP servers, allowing direct interaction with resources such as logs, repositories, and design assets. It acts as a central client for structured, context-aware AI tasks.
Link: Claude Desktop
5. Sourcegraph Cody
Cody integrates MCP to analyze large codebases efficiently. It can fetch, summarize, and reason over repository data, making it particularly effective for teams managing monorepos or complex dependency structures.
Link: Cody
6. Cursor IDE
Cursor IDE embeds MCP deeply into its workflow. It connects with servers like Appwrite and GitHub to provide real-time code understanding, context retrieval, and project-level automation during development.
Link: Cursor
7. Windsurf IDE
Windsurf focuses on agent-based coding. Its MCP integration allows developers to link multiple servers, automate build and test processes, and execute tool-assisted operations across environments.
Link: Windsurf IDE
8. Open Code
Open Code emphasizes transparency and control. As an MCP client, it allows developers to configure how data and commands are exchanged with connected servers, making it a strong fit for teams that prioritize self-hosting and security.
Link: Open Code
9. Goose
Goose is a lightweight MCP client built for experimentation. It’s commonly used for testing server responses, validating configurations, and debugging new MCP integrations in local or development environments.
Link: Goose
Wrapping up
The MCP ecosystem is becoming an important part of how developers connect tools, data, and AI systems. Servers expose useful capabilities, and clients make those capabilities accessible within the environments developers already use.
Appwrite has added two servers that fit naturally into this ecosystem. MCP server for Appwrite API lets AI tools interact with Appwrite projects to create users, manage databases, and handle resources through simple prompts. The MCP server for Appwrite docs complements it by allowing clients to access Appwrite’s documentation contextually, so information and references are always within reach.
