Developed by Anthropic, MCP (Model Context Protocol) servers are lightweight programs that expose specific capabilities to hosts (systems like IDEs) via this standard interface. They are useful for developers building AI applications needing context, from AI-powered IDEs to chat interfaces like ChatGPT. But what type of MCP servers are there, and what are the best MCP servers?

This blog highlights the 10 best MCP servers, covering areas like version control, cloud storage, databases, and web search, to show you how they can significantly enhance your AI projects.

What is an MCP Server?

The Model Context Protocol (MCP) is an open standard that acts like a universal connector for AI applications. MCP allows AI to access external tools and data sources, such as cloud storage or code repositories, in a standardized way. This makes it easier to build AI-powered tools that can interact with the real world, from automating tasks to fetching live data. Want to create your own MCP servers? Here’s how you do it.

Why These Servers?

The 10 best MCP servers highlighted below are chosen for their versatility and popularity within the MCP ecosystem. They cover essential functions like version control, communication, and data management, making them valuable for developers and businesses looking to integrate AI into their workflows. Each server offers unique features to enhance AI applications.

10 Best MCP Servers

1. GitHub MCP Server

The GitHub MCP Server integrates AI assistants with GitHub’s API, allowing seamless management of repositories, issues, pull requests, branches, and releases. With robust authentication and error handling, this server transforms how developers interact with their code repositories.

What It Does

• Provides access to GitHub’s API for repository management.
• Supports operations like creating issues, reviewing pull requests, and managing releases.

Use Cases

• Automating code reviews with AI assistance.
• Generating release notes based on commit history.
• Searching and retrieving code snippets from repositories.
• Managing pull requests and issues through natural language queries.

Notable Features

• Supports OAuth for secure authentication.
• Handles rate limiting and retries for API requests.
• Provides detailed error messages for debugging.

Why It’s Essential

For developers and teams using GitHub, this server is a must-have. It bridges the gap between AI and version control, enabling intelligent workflows that save time and improve collaboration. Available via the MCP Servers Repository.

2. Slack MCP Server

The Slack MCP Server enables AI assistants to integrate into Slack workspaces, allowing real-time message posting, user profile retrieval, channel management, and emoji reactions. This integration automates workflows and enhances team productivity within Slack environments.

What It Does

• Connects AI models to Slack’s API for messaging and channel management.
• Supports posting messages, reacting to messages, and retrieving user data.

Use Cases

• Automating responses to common queries in Slack channels.
• Summarizing channel discussions for quick updates.
• Managing tasks and reminders through AI-driven bots.
• Integrating with other tools via Slack’s ecosystem.

Notable Features

• Supports multiple workspaces and channels.
• Handles message threading and emoji reactions.
• Provides access to user profiles and channel metadata.

Why It’s Essential

Slack is a central hub for team communication, and integrating AI through this server allows for smarter, more automated workflows. It’s perfect for teams looking to enhance productivity with AI-driven assistance. Available via the MCP Servers Repository.

3. Google Drive MCP Server

The Google Drive MCP Server allows AI applications to access and manage files on Google Drive, including file operations, searching, and metadata retrieval. This is particularly useful for applications that need to work with documents, spreadsheets, and other cloud-stored files.

What It Does

• Provides read and write access to Google Drive files.
• Supports file searching, downloading, and uploading.

Use Cases

• Retrieving and processing documents for analysis.
• Backing up and restoring files programmatically.
• Searching for specific files based on metadata or content.
• Collaborating on documents with AI assistance.

Notable Features

• Supports file sharing and permission management.
• Handles large files and streaming for efficient data transfer.
• Integrates with Google Drive’s advanced search capabilities.

Why It’s Essential

Google Drive is a widely used cloud storage solution, and this server enables AI applications to interact with it seamlessly. It’s ideal for applications that require access to user files or need to store and retrieve data in the cloud. Available via the MCP Servers Repository.

4. Qdrant MCP Server

The Qdrant MCP Server integrates with Qdrant, a vector search engine, allowing AI agents to store and retrieve information semantically. It functions as a semantic memory layer, enabling LLM applications to access and manage memories within a Qdrant database.

What It Does

• Provides semantic search and memory management using Qdrant.
• Supports storing and retrieving vector embeddings for context-aware applications.

Use Cases

• Building semantic memory for AI agents to remember past interactions.
• Retrieving code snippets or documentation based on semantic meaning.
• Enhancing chatbots with context-aware responses using stored memories.

Notable Features

• Supports vector search for efficient similarity matching.
• Allows for dynamic memory management and retrieval.
• Integrates with various data sources for enriched context.

Why It’s Essential

For applications that require advanced search capabilities based on vector embeddings, Qdrant MCP Server is invaluable. It enables AI models to understand and retrieve information based on context, making interactions more natural and relevant. Available via the Qdrant MCP Server Repository.

5. Puppeteer MCP Server

The Puppeteer MCP Server enables browser automation, allowing AI applications to interact with web pages, scrape content, and perform actions like filling forms or clicking buttons. This is powerful for tasks that require interacting with dynamic web content.

What It Does

• Automates browser interactions using Puppeteer.
• Supports web scraping, form filling, and page navigation.

Use Cases

• Web scraping for data collection.
• Automating web-based workflows like login processes.
• Testing web applications with AI-driven scripts.
• Extracting information from JavaScript-heavy websites.

Notable Features

• Supports headless browser mode for server-side execution.
• Handles complex web interactions with JavaScript support.
• Provides screenshot and PDF generation capabilities.

Why It’s Essential

For applications that need to interact with the web, this server is invaluable. It allows AI models to perform tasks that would otherwise require manual intervention, making it a powerful tool for automation. Available via the MCP Servers Repository.

6. Brave Search MCP Server

The Brave Search MCP Server integrates web and local search capabilities using Brave’s Search API. It offers pagination, filtering, safety controls, and smart fallbacks, making it a privacy-focused search tool for AI applications.

What It Does

• Provides web search functionality with privacy-first features.
• Supports filtering and pagination for search results.

Use Cases

• Performing web searches within AI conversations.
• Retrieving up-to-date information for Retrieval-Augmented Generation (RAG) systems.
• Enhancing AI applications with search capabilities without compromising user privacy.

Notable Features

• Respects user privacy with no tracking.
• Supports various search filters and safety controls.
• Integrates with local search capabilities for comprehensive results.

Why It’s Essential

In an era where privacy is a growing concern, this server offers a secure way to integrate search functionality into AI applications. It’s ideal for developers who prioritize user data protection. Available via the MCP Servers Repository.

7. Docker MCP Server

The Docker MCP Server allows for the execution of isolated code in Docker containers. It supports multi-language scripts, dependency management, and error handling, providing a secure environment for running untrusted or experimental code.

What It Does

• Executes code in isolated Docker containers.
• Supports various programming languages and dependency management.

Use Cases

• Running code snippets safely without risking the host system.
• Testing code in isolated environments.
• Deploying microservices with AI assistance.
• Automating build and deployment processes.

Notable Features

• Ensures isolation and security through containerization.
• Supports multiple programming languages.
• Automatically manages dependencies for executed code.

Why It’s Essential

For developers working with untrusted or experimental code, this server provides a secure sandbox. It’s also useful for automating deployment tasks in AI-driven development workflows. Available via the Docker MCP Server.

8. Notion MCP Server

The Notion MCP Server integrates with Notion’s API, allowing AI applications to read and write to Notion databases, pages, and blocks. This is useful for applications that need to interact with Notion’s flexible content management system.

What It Does

• Provides access to Notion’s API for content management.
• Supports CRUD operations on Notion pages and databases.

Use Cases

• Automating content creation in Notion.
• Retrieving and analyzing data from Notion databases.
• Integrating Notion with other tools for enhanced productivity.
• Enhancing workflows with AI-assisted Notion interactions.

Notable Features

• Supports rich text and media content.
• Handles Notion’s search and filtering capabilities.
• Allows for real-time updates and synchronization.

Why It’s Essential

Notion is a popular tool for note-taking and project management, and this server enables AI applications to interact with it seamlessly. It’s perfect for teams already using Notion who want to leverage AI for automation. Available via the Notion MCP Server.

9. Make MCP Server

The Make MCP Server (formerly Integromat) allows AI applications to trigger and control Make scenarios, turning complex workflows into callable tools. This is powerful for automating business processes and integrating with various APIs.

What It Does

• Connects AI models to Make’s workflow automation platform.
• Supports triggering and managing Make scenarios.

Use Cases

• Automating marketing campaigns with AI-driven triggers.
• Managing customer data flows across multiple tools.
• Integrating with CRMs and other business tools.
• Creating custom workflows on the fly with AI assistance.

Notable Features

• Supports a wide range of integrations with other tools.
• Allows for conditional logic and branching in workflows.
• Provides real-time monitoring and logging of workflow execution.

Why It’s Essential

For businesses looking to automate complex processes, this server is a game-changer. It allows AI to control and enhance workflow automation, making it a valuable tool for productivity. Available via the Make MCP Server.

10. FileSystem MCP Server

The FileSystem MCP Server provides direct access to the local file system with configurable permissions. It enables AI models to read, write, and manage files within specified directories, making it essential for applications that need to work with local files.

What It Does

• Grants secure access to local file systems.
• Supports file operations like reading, writing, and deleting.

Use Cases

• Reading configuration files for AI applications.
• Writing logs or output files from AI processes.
• Managing datasets for machine learning tasks.
• Integrating with local development environments.

Notable Features

• Configurable access controls for security.
• Supports file operations like copy, move, and delete.
• Handles large files and directories efficiently.

Why It’s Essential

For applications that require direct interaction with local files, this server is indispensable. It provides a secure way to manage file operations while ensuring AI models can access the necessary data. Available via the MCP Servers Repository.

FAQ about the Best MCP Servers

1. What specific problem does the MCP solve for AI applications like LLMs? 

The MCP solves the problem of allowing AI applications, particularly LLMs, to securely access and interact with external tools and data sources in a standardized way. It acts like a “universal connector,” enabling AI to go beyond its training data and interact with real-world services such as cloud storage or code repositories.

2. How do MCP servers, like the GitHub or Slack servers, allow AI to perform actions on external platforms? 

MCP servers achieve this by integrating AI assistants directly with the APIs of these external platforms. For example, the GitHub MCP Server integrates with GitHub’s API to allow AI to manage repositories, issues, and pull requests, while the Slack MCP Server connects to Slack’s API for tasks like posting messages and managing channels, effectively turning platform capabilities into features AI can utilize.

3. What kinds of external data and knowledge can AI access or manage using MCP servers mentioned in the article? 

AI applications can access and manage various types of external data. The article highlights servers that allow access to files on Google Drive, perform semantic search and manage vector embeddings in Qdrant (acting as a semantic memory), and read/write to databases, pages, and blocks in Notion. The FileSystem MCP Server also provides direct access to local files within specified directories.

4. Beyond accessing data, what types of automation or task execution can AI perform using some of the listed MCP servers? 

AI can perform significant automation tasks. The Puppeteer MCP Server allows AI to automate browser interactions like web scraping or filling forms. The Make MCP Server enables AI applications to trigger and control complex workflow scenarios defined in Make. The Docker MCP Server provides a secure environment for AI to execute isolated code in various programming languages.

5. Does the MCP standard incorporate security features for connecting AI to external services? 

Yes, MCP servers are designed to expose capabilities securely via the standard interface. It mentions features like robust authentication (e.g., OAuth for the GitHub server) and configurable access controls (specifically for the FileSystem server) as part of these integrations.

6. Where can developers find detailed information to get started with building or using MCP servers? 

Developers can find detailed documentation and setup guides by visiting the MCP Official Website or exploring the MCP Servers Repository.

The Bottom Line

The best MCP servers help connect AI with other tools, making applications more powerful and secure through standardization. By exploring these options, developers and organizations can increase innovation and efficiency. As the MCP ecosystem grows, there will be even more ways to link AI to real-world applications. You can find resources to get started at the MCP Official Website or the Servers Repository.