AI in Daily Coding and the Need for MCP
We’ve all encountered AI in our day – to – day coding tasks. Tools like Replit, an online code editor with built – in AI for code generation and guidance, and GitHub Copilot for VS Code, which enhances the coding workflow through simple extensions, are becoming increasingly popular. And for those who have tried Black Box AI or Cursor IDE, the convenience of in – built AI assistance is well – known.
However, a significant challenge exists. AI assistants need access to various data sources, but currently, each new data source often requires custom code. This is not only a time – consuming process but also makes the overall integration messy. The Model Context Protocol (MCP) aims to address this issue.
What is MCP?
MCP is an open standard that establishes secure, two – way connections between your data and AI – powered tools. It can be thought of as a USB – C port for AI applications, serving as a single, common connector that enables different tools and data sources to communicate with each other. For developers, instead of writing a custom connector for every new data source, they can build against this one standard protocol. For AI tools, MCP allows them to access the necessary information, regardless of where it is stored.
Why MCP Matters
As AI assistants become an integral part of our daily workflow, ensuring they have all the required context is crucial. MCP simplifies the situation in several ways. It offers pre – built integrations through a growing library of ready – to – use connectors. It provides flexibility by allowing easy switching between different AI providers. Additionally, it enhances security by following best practices to keep data safe within your own infrastructure.
Dhanji R. Prasanna, CTO at Block, said, “At Block, open source is more than a development model—it’s the foundation of our work and a commitment to creating technology that drives meaningful change. Open technologies like the Model Context Protocol connect AI to real – world applications in an accessible, transparent, and collaborative way.”
Real – World Impact and Early Adoption
Companies like Block and Apollo are among the early adopters, integrating MCP into their systems. Development tool companies such as Zed, Replit, Codeium, and Sourcegraph are also exploring MCP. This adoption means that AI agents can retrieve more relevant information for coding tasks, leading to higher – quality code with fewer iterations.
How MCP Works
MCP follows a client – server architecture. MCP Hosts are programs that want to access data via MCP, such as Claude Desktop or popular IDEs. MCP Clients maintain a 1:1 connection with MCP servers, which are lightweight adapters that expose specific data sources or tools. Local data sources include your computer’s files, databases, and services, while remote services are external systems like GitHub or Slack accessible over the internet.
The connection lifecycle of MCP includes initialization, where the client and server exchange protocol version and capabilities; message exchange, which supports request – response and notification patterns; and termination, which can occur via clean shutdown, transport disconnection, or error conditions.
Watch it in Action
A quick demo using the Claude desktop app shows how, with MCP configured, Claude can directly connect to GitHub, create a new repo, and make a PR through a simple integration. Once set up, building this integration took less than an hour.
What Experts Are Saying
Alex Albert (@alexalbert__) on X explains that getting LLMs to interact with external systems is usually difficult as developers need to write custom code. MCP fixes this with a standard protocol for sharing resources, tools, and prompts. It has a unified architecture, can expose not just data but also tools and prompts, and has built – in security.
Get Started with MCP
MCP is designed for quick setup. You can have a working integration in under 5 minutes with pre – built servers for platforms like GitHub, Slack, SQL databases, local files, and search engines. There is a step – by – step guide to install the Claude Desktop app and connect your first server, and resources for server developers to build their own MCP servers.
Tools in MCP
Tools in MCP allow servers to expose executable functions. Clients can discover available tools using the tools/list endpoint and call them via the tools/call endpoint. Tools can range from simple to complex operations, and each tool has a unique name, an optional description, and a JSON schema for parameters.
Latest Outcomes and Future Developments
The Model Context Protocol has seen several recent developments, such as the release of Java, Python, and Kotlin SDKs with various improvements. For the first half of 2025, priorities include remote MCP support, reference implementations, better distribution and discovery, agent support, and expanding the ecosystem.
Conclusion
MCP is set to revolutionize the way AI accesses data sources. Just as home Wi – Fi and power outlets provide universal and standard ways for devices to connect and function, MCP acts as a universal connector for AI tools and data sources. By replacing a tangle of custom connectors with a single protocol, MCP will simplify developers’ work and enhance our daily experiences with more powerful, context – aware AI tools.
