In the rapidly evolving AI landscape, one of the most exciting developments is the Model Context Protocol, or MCP. This open-source protocol is transforming how large language models (LLMs) interact with external tools and data sources, enabling smarter, more context-aware AI applications. As someone deeply fascinated by AI and its real-world applications, I want to take you on a detailed journey into MCP — what it is, why it matters, and how you can start building your own MCP-enabled applications, especially using SingleStore as a powerful backend.
Whether you’re a developer, AI engineer, or data scientist, this guide will provide a clear, step-by-step walkthrough and practical insights to help you harness MCP and elevate your AI projects. Let’s dive right in!
What is MCP? An Introduction to Model Context Protocol
MCP stands for Model Context Protocol. At its core, MCP is an open-source standard initially developed by Anthropic to standardize the way AI systems, particularly large language models, interact with external tools and data sources.
Why is this important? Traditional LLMs are incredibly powerful but limited by their training data, which is static and can quickly become outdated. While retrieval-augmented generation (RAG) techniques allow LLMs to access external knowledge bases or documents, they fall short when it comes to interacting with dynamic tools or performing actions beyond reading data. This is where MCP shines.
MCP allows LLMs to access real-world data and applications beyond their initial training datasets. It enables AI agents to perform actions like querying databases, managing projects, or even creating notebooks — all in a standardized, secure, and scalable way.
Image credits Descope
Think of MCP as a universal remote or a USB-C port for AI applications: it provides a universal interface to connect any tool, service, or data source seamlessly to your AI models. This opens the door to building agentic AI applications that can automate complex workflows and interact with multiple external systems effortlessly.
Key Components of MCP
MCP image from the report
To understand how MCP works, it’s crucial to know its three main components:
Hosts: These are AI-powered applications where users interact with the AI, such as cloud desktops, integrated development environments (IDEs), or chatbots. This is your playground where the magic happens.
Clients: These modules exist within the host applications and manage the connections to servers. They act as intermediaries, facilitating communication between the host and the external resources.
Servers: These are wrappers around external tools or data sources, exposing their capabilities to AI applications in a standardized way. Examples include a GitHub server or a SingleStore server, each representing a specific external system that the AI can interact with.
By structuring MCP this way, the protocol ensures modularity and flexibility, making it easy to add or swap out servers without disrupting the overall system.
How MCP Works: A Practical Overview
Credits: MCP workflow image by the report Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
As we already know, the MCP (Model Context Protocol) workflow demonstrates how AI agents and applications can seamlessly access and utilize external resources through a standardized protocol. The process begins when a user submits a prompt (like requesting the latest AAPL stock price via email) to MCP Hosts such as chat applications, IDEs, or AI agents.
These hosts perform intent analysis to understand the request, then communicate through a Transfer Layer that handles the initial request, response, and notifications between clients and servers in a 1:1 relationship. The MCP Servers, which include various services like development tools, databases, and applications (represented by icons for services like GitHub, Gmail, Google Drive, and SQLite), receive these requests and leverage their specific capabilities — including access to Tools, Resources, and Prompts. Based on the request requirements, the servers perform tool selection and orchestration, potentially invoking APIs to access external data sources such as web services, databases, or local files.
The system can also trigger notifications and sampling mechanisms as needed, ultimately delivering the requested information back through the same pathway to fulfill the user’s original request, creating a comprehensive ecosystem where AI applications can securely and efficiently interact with diverse external resources and services.
Much Simpler MCP Flow
When a user interacts with an MCP-enabled AI application, the AI uses the protocol to access external information or trigger actions in other applications.
For example, you could ask the AI to:
- Search a database for specific information
- Create a task in a project management tool
- Add dummy data to your database for testing
- Create a notebook environment for data analysis_
These operations happen in real-time, enabling the AI to be context-aware and agentic — meaning it can act autonomously based on the context it has gathered externally.
MCP Through Practical Hands-On
Step 1: Meet SingleStore — The Ideal Database for MCP
For MCP to be truly powerful, it needs a backend that can keep up with real-time data needs and support versatile querying capabilities. This is where SingleStore comes in.
SingleStore is a relational database that supports vector data and hybrid search, making it perfect for RAG applications and serving as a vector database for AI models. Its high performance and real-time capabilities make it an excellent choice for MCP servers.
With SingleStore, you can store, query, and manage your data efficiently, and integrate it seamlessly with MCP to empower your AI applications.
Step 2: Setting Up Your SingleStore MCP Server
Setting up your own SingleStore MCP server is simpler than you might think. Here’s a step-by-step guide to get you started:
Clone the GitHub Repository: The SingleStore MCP server is open source and available on GitHub. The repository includes an installer and the MCP server code, enabling seamless integration.
Prepare Your Environment: Ensure you have Python installed, along with necessary dependencies such as uvicorn. You’ll also need a SingleStore account, which offers a free tier with credits to get you started.
Initialize the MCP Server: Using the repository’s provided commands, run the init command in your terminal or VS Code. This sets up the MCP server quickly and efficiently.
Authenticate with SingleStore: You’ll need to authenticate your SingleStore account to allow the MCP server to access your databases securely.
Connect Your MCP Client: Use an MCP-enabled client like a cloud desktop or chatbot to connect to your SingleStore MCP server. This client will manage interactions between you and the server.
Once connected, you are ready to start interacting with your SingleStore database through MCP!
Step 3: Exploring MCP Server Capabilities with SingleStore
With your SingleStore MCP server up and running, you can now explore various operations that showcase how MCP enhances AI capabilities.
Creating a Database: Start by asking your MCP client to create a new database. For example, you can say, “Create a database named test in my workspace.” The MCP server will handle the request, authenticate your workspace, and create the database for you.
This process demonstrates how MCP abstracts away the complexity of database management and lets you operate with natural language commands.
Adding Dummy Data: Next, you can instruct the MCP client to add dummy data to your new database. The server will:
- Create tables such as
employees,products, andorders - Populate these tables with sample records
- Generate SQL commands behind the scenes to execute these tasks
For instance, the server might create an employees table with columns like first name, last name, email, department, and salary, then insert several sample employee records.
This feature is invaluable for developers and data scientists who want to quickly prototype and test SQL queries or AI workflows without manually setting up data.
Running Queries and Analyzing Data: After populating your database, you can query it using natural language or SQL commands. For example, you might ask for “all employees grouped by department,” and the MCP server will execute the SQL query and return aggregated data like employee counts and average salaries per department.
This capability enables dynamic data exploration and empowers AI agents to provide actionable insights based on real-time data.
Here is the complete step-by-step video tutorial below.
Step 4: Verifying Your Data in SingleStore
It’s always good to verify that the MCP server executed your commands correctly. You can log in to your SingleStore account and navigate to your workspace to check the following:
- The newly created database (e.g., test)
- The tables created (employees, products, orders)
- Sample data inserted into these tables
By viewing the data directly in SingleStore’s dashboard or data studio, you gain confidence that your MCP server is working as expected and that your AI client can interact with the database seamlessly.
Step 5: Automating Workflows with MCP Servers
Beyond simple CRUD operations, MCP servers open the door to automating complex workflows. Here’s what you can do:
- Schedule jobs to run at specific intervals
- Create and manage notebooks for data analysis
- Take snapshots of your database state
- Trigger actions in external applications based on AI decisions
By building MCP servers around your favorite tools and services, you can create a unified AI ecosystem where your models not only understand data but also act on it intelligently and autonomously.
Why MCP is a Game-Changer for AI Applications
The promise of MCP lies in its ability to overcome limitations that have traditionally held back LLMs:
- Real-Time Context: MCP enables LLMs to access up-to-date information rather than relying solely on static training data.
- Tool Integration: LLMs can interact with a wide range of external tools, from databases to project management apps, expanding their usefulness.
- Standardization: MCP provides a standardized protocol, meaning developers can build modular, interoperable AI systems without reinventing the wheel.
- Agentic AI: With MCP, AI agents can take autonomous actions based on context, opening new horizons for automation and intelligent decision-making.
For anyone serious about building next-generation AI applications, understanding and leveraging MCP is essential.
Final Thoughts and Next Steps
Model Context Protocol is truly a revolutionary step forward in making AI models smarter, more flexible, and more capable of interacting with the real world. By standardizing how AI connects to external data and tools, MCP unlocks new possibilities for building agentic AI applications that can automate workflows, analyze data, and perform tasks autonomously.
Using SingleStore as an MCP server backend provides a robust, high-performance platform that supports complex querying and vector search, making it an ideal partner for MCP-powered AI systems.
If you’re eager to get hands-on, I highly encourage you to visit the SingleStore MCP server GitHub repository, sign up for a free SingleStore account, and try setting up your own MCP server. Experiment with creating databases, adding dummy data, and running queries. This practical experience will deepen your understanding of MCP and prepare you to build powerful AI applications.
Remember, the future of AI is not just about smarter models but about smarter interactions — and MCP is leading the way.
Useful Links to Get Started
Thanks for joining me on this deep dive into MCP. I hope this guide empowers you to explore and innovate with this exciting protocol.
Happy building!