You have 1 article left to read this month before you need to register a free LeadDev.com account.
Estimated reading time: 7 minutes
Your inbox, upgraded.
Receive weekly engineering insights to level up your leadership approach.
The rapid evolution of artificial intelligence has completely transformed the way we interact with technology.
Large language models (LLMs) have unlocked remarkable capabilities, yet integrating these models with external data sources and tools has remained a persistent challenge.
When Anthropic introduced the Model Context Protocol (MCP) in November 2024, it brought a standardized approach to connecting AI systems like ChatGPT or Claude with external tools, APIs, and data sources.
Since then it has seen rapid adoption, being brought into most integrated development environments (IDEs) and getting the stamp of approval from Google CEO Sundar Pichai in April. Google Deepmind cofounder and CEO Denmis Hassabis has also praised MCP, confirming that Google’s Gemini models and SDK will support it.
The challenge of AI integration
Before MCP, integrating AI models with databases, APIs, or local resources often required custom-built function calls, an approach that was both cumbersome and difficult to scale.
Every new tool required a separate integration, creating a maintenance nightmare. This increased the operational burden on developers and introduced the risk of AI models generating misleading or incorrect responses due to poorly defined integrations.
Techniques like retrieval-augmented generation (RAG) and function calling have been used to extend an LLM’s capabilities beyond its training cutoff, but they rely on custom API integrations for each tool. This means every external system an AI model interacts with requires a bespoke implementation, and if the API changes, developers must manually update the integration. The lack of standardization makes large-scale AI deployments complex and inefficient.
When AI models like GPT are integrated with external tools or APIs, they rely on those integrations to fetch or manipulate data in the real world. If those integrations are poorly defined, it means:
- Ambiguous or incomplete specifications. The AI doesn’t fully understand what a tool does or what kind of input/output it expects.
- Lack of standardized responses. The model might misinterpret the structure or meaning of what it receives from an external system.
- Too many tool choices with unclear distinctions. When the AI sees multiple vaguely defined options, it might pick the wrong one.
- Mismatch between tool functionality and AI expectations. If the API does one thing but the model assumes it does another, the model might confidently output incorrect or misleading info.
The risk isn’t just that the integration fails, it’s that it partially works, and the AI “fills in the gaps” with plausible-sounding but incorrect information. That’s where hallucinations come from in this context.
Enter MCP
MCP solves this problem by providing an open-source protocol that streamlines how AI models interact with external services. Instead of bespoke integrations, MCP acts as a standardized bridge, allowing AI applications to tap into diverse data sources without requiring developers to rewrite their connection logic repeatedly.
The architecture follows a simple host-client-server model:
- Hosts are AI applications that need access to external data. Examples include OpenAI’s ChatGPT, Anthropic’s Claude, and AI-powered IDEs like Cursor.
- Clients handle the communication between hosts and servers, ensuring requests are properly routed and processed.
- Servers expose tools, resources, and prompts in a standardized manner, allowing the AI to interact with databases, APIs, or local storage seamlessly.
The MCP Server is particularly crucial, as it consists of three core components:
- Resources: Data repositories or structured knowledge bases that AI can query for factual and contextual information.
- Tools: Functional components that enable AI to interact with APIs, databases, or software systems dynamically.
- Prompts: Predefined templates or instruction sets that help AI applications generate contextually accurate responses.
This modular approach makes integrating AI with real-world data far more manageable. It abstracts away the complexity of manually managing tool integrations, reducing the potential for errors and making AI applications more reliable.
More like this
Why MCP is gaining traction
MCP’s growing popularity is not just about big name support from companies like OpenAI and Google. It addresses a fundamental need in the AI ecosystem: a standardized way for LLMs to interact with external tools. In many ways, it’s akin to the evolution of REST APIs or the Language Server Protocol (LSP). Just as LSP standardized how development environments integrate with various programming languages, MCP is doing the same for AI models and their data sources.
Moreover, MCP’s open-source nature makes it a compelling choice for enterprises and developers alike. Unlike proprietary integration solutions, MCP ensures interoperability across different AI models and platforms. This fosters a more collaborative AI development environment where tools and services can be shared and improved collectively.
How MCP works in practice
Let’s break down a practical example of how MCP functions. Suppose an AI application needs to summarize the last five commits from a GitHub repository. Without MCP, developers would have to manually integrate GitHub’s API, handle authentication, and write logic to parse the results.
With MCP, the workflow is much simpler:
- The AI application (MCP Host) sends a request to the MCP Client, asking what tools are available.
- The MCP Client queries the MCP Server to retrieve a list of tools (e.g., GitHub commit retrieval, Jira issue tracking, etc.).
- The AI receives the tool list and selects the appropriate one.
- The MCP Client sends the request to the MCP Server, which fetches the commit data from GitHub.
- The results are returned to the AI, which incorporates them into its response.
This abstraction means developers no longer need to worry about managing multiple APIs or updating integrations when external services change. The MCP standard ensures that AI models can access the latest data without requiring direct modifications to their integration logic.
Potential challenges
While MCP is incredibly promising, there are plenty of potential challenges. One key concern is security. Since MCP servers expose tools and data to AI applications, organizations must ensure that sensitive information remains protected. Developers should carefully vet MCP implementations, especially open-source servers, to prevent unauthorized access or data leaks.
Another consideration is that MCP, like any emerging technology, is still evolving. While it is gaining traction, other competing standards may emerge. Companies adopting MCP should remain flexible and be prepared for future developments in AI integration protocols.
Additionally, MCP is not a one-size-fits-all solution. In cases where only a few tools need to be integrated, traditional function calling may still be more practical. MCP shines in scenarios where multiple external systems must interact with AI models dynamically.
The future of AI integrations
As AI continues to advance, the need for standardized, scalable integration methods will only grow. MCP isn’t just a technical improvement; it’s a critical step toward making AI truly interoperable with the vast ecosystem of tools and services available today.
As more MCP servers pop up for the same underlying APIs, I’ve noticed a pattern starting to emerge. Call it the MCP Maturity Model, and it typically plays out in three stages:
1. Just doing something. This is the MVP stage. At first, most MCP servers just expose a few useful parts of an API, usually the easy, REST-style endpoints. It’s enough to get something working and test the concept, but it leaves out more complex features that don’t fit neatly into that mold.
2. Covering the API. With a bit more effort, teams start to expose those harder-to-model parts of the API too. Now, the LLM can interact with the full set of operations – everything the API offers – not just the basics. It takes more thought, but it gives the AI much richer access.
3. Serving the user. This is where things get interesting. Once LLMs start interacting with the API regularly, users begin to expect more. At this point, it’s not just about translating existing functionality into MCP. It’s about adapting to new use cases, maybe by improving the API itself, or building new logic into the MCP server to meet those needs directly.
London • June 16 & 17, 2025
Speakers Gergely Orosz, Camille Fournier and Lara Hogan confirmed.
Final thoughts
MCP presents an incredible opportunity by simplifying how AI systems interact with external data, reducing maintenance overhead while making integrations more scalable. More importantly, it accelerates innovation by removing the complexity of managing multiple tool-specific connectors.
Getting started with MCP isn’t difficult. Anthropic provides extensive documentation, and there are open-source implementations available on GitHub. Community-driven repositories like the Model Context Protocol GitHub Repository showcase real-world examples of how developers are leveraging this protocol.
The future of AI isn’t just about improving model performance; it’s about making these models more accessible, dynamic, and adaptable. MCP is a major step in that direction, and we’re only beginning to see its full impact on the industry.
