As AI applications increasingly rely on structured data in real-time, MCP servers are becoming more and more important. These servers provide the means to connect LLMs with real-time data streams in a way that AI systems can reason concerning current, contextually relevant data. There are several options in the commercial space; however, open-source options are gaining traction as they are easier to audit, adapt, and often come with greater community support. These tools are great for developers who are building AI agents, copilots, or assistants focused on a specific domain. In this blog, we will go over what MCP servers are and their different types.
MCP Servers and Their Features
An MCP server, or MCP Server, is a type of server that can deliver real-time, structured, and relevant information to large language models (LLMs) or AI agents on inference, or as they perform tasks. These servers act as context servers, supplementing LLMs with new available external structured information about which the LLM was not trained.
Learn more about MCP servers here.
Key Features
Now, let’s look at the features of the MCP server that make it efficient.
- Tool Invocation: MCP servers expose functions, or tools, which can be invoked by LLMs to perform specific tasks, e.g., querying a database or sending a message. These tools are defined in a standardized way and can be used by AI models.
- Resource Access: They provide access to either static or dynamic data, called “resources”, which LLMs can query to extract data to include in responses. This enables models to provide responses that use the most current data to ensure improved accuracy and relevance
- Prompt Templates: MCP servers can provide pre-defined prompts to assist LLMs in interacting with tools, as well as resources. These templates can also help to standardize interactions and help improve the consistency in outputs of the AI.
- Capability discovery: Once a connection has been made, MCP clients can call servers to find out what tools, resources, and prompts can be discovered via the dynamic discovery process. AI applications can adapt to different MCP servers without needing to be set up manually.
- Flexible communication protocols: MCP supports many ways of communicating, including standard input/output for integrations with local resources, and that may include local services, and HTTP with Server-Sent Events(SSE) for remote connections. This ensures that all possible deployment environments are accommodated.
Popular MCP Servers
Now, you know what MCP servers are and their features, let’s explore some popular ones:
File System MCP Server
The File System MCP Server provides a way for AI assistants to interact securely with your file system running locally or remotely. It provides a controlled way for AI assistants to interact with files and directories for reading, writing, editing, or organizing files. It is ideal for activities involving coding assistants, automation, and document management.
Features:
- File interactions include: List, read, write, append, delete
- Edit files with pattern matching
- Directory interactions include: Create, List, move, delete
- Search for files and directories by name or pattern
- Fetch file metadata (size, timestamps)
GitHub MCP Server
The GitHub MCP Server provides an interface for AI applications to interact directly with GitHub, allowing the application to read and update repositories, manipulate code, issues, and pull requests, and automate common development workflows.
Features:
- Other key functions include:
- Enumerate repositories and branches
- Read and update files in repositories
- Create and merge pull requests and issues
- Search across code and repository metadata
Slack MCP Server
The Slack MCP Server allows AI agents to interface with and automate Slack workspaces so that they can communicate in real-time, notify users, or trigger workflows on teams.
Features:
- Send and receive messages in channels or direct messages
- Search in channels and message history
- Automate notifications and reminders
- Manage channels and users
- OAuth-based authentication to make sure access is secure
Google Drive MCP Server
The Google Drive MCP Server allows AI assistants to securely connect to Google Drive so that they can search, read, and organize documents and files in the cloud.
Features:
- List, read, and write files and folders
- Search for documents by name or content
- Organize files into folders
- Manage sharing and permissions
- Use OAuth for user privacy
Docker MCP Server
Docker MCP Server facilitates AI-driven management for Docker containers, images, and volumes, enabling DevOps automation and infrastructure orchestration.
Features:
- List, start, stop, and remove containers.
- Manage images and volumes,
- Access logs and container state,
- Deploy and update stacks,
- Secure, permissioned access.
Perplexity MCP Server
The Perplexity MCP Server connects AI assistants to the Sonar API from Perplexity, which allows for much easier and current web searching and information for research-type tasks and dynamic knowledge tasks.
Features:
- Search the web live
- Get answers summarized or with the source
- Get current news and facts
- Integrate results into AI work
- Clarity for API key management
Puppeteer MCP Server
The Puppeteer MCP Server enables AI agents to robotically automate browser tasks, interact with websites, and extract web data through headless browser scripting.
Features:
- Automates web browsing; fills out forms
- Scrape a web page’s contents and metadata
- Take screenshots or generate PDFs of web pages
- Simulate user interactions (clicks, typing)
- Safe, secure, sandboxed execution environment
If you want to explore more, you can visit this GitHub page to find more useful MCP servers.
Hands-On with MCP Servers
Now that we have learned about a few of the popular MCP servers, let’s see them in practice when integrated with Claude Desktop.
We will use the File System MCP Server to determine how many folders are on my desktop, the GitHub MCP Server to get the repositories on my GitHub account, and also use it to go to the Analytics Vidhya blog web page.
Conclusion
Popular MCP servers are rapidly emerging as a crucial component to create smarter, responsive AI applications by connecting models to live and structured data. Open source servers offer the most flexibility in terms of how to use and connect the processes to the models, while benefitting from a strong community support network. The benefit of an MCP server is that no matter the use case, a GPT AI assistant interacting with files, automating a Slack channel, or pulling live data from the internet, users will have a better experience and an easy way to ground an AI into live and relevant context. As AI continues to adapt and grow, our adoption of MCP servers is key to making AI not only useful but contextual and responsive.
Data Scientist | AWS Certified Solutions Architect | AI & ML Innovator
As a Data Scientist at Analytics Vidhya, I specialize in Machine Learning, Deep Learning, and AI-driven solutions, leveraging NLP, computer vision, and cloud technologies to build scalable applications.
With a B.Tech in Computer Science (Data Science) from VIT and certifications like AWS Certified Solutions Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Fake News Detection, and Emotion Recognition. Passionate about innovation, I strive to develop intelligent systems that shape the future of AI.
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