How Model Context Protocol Servers can revolutionise our use of AI tools
AI is a hot topic, with game-changing tools being released at an increasing rate. It’s incredibly difficult to identify those which genuinely offer a unique selling point.
As I spend more time in the AI space, I see fewer truly revolutionary approaches. Many generative tools out there are just UI wrappers to GPTs or other services with well-crafted prompts – nothing you can’t implement yourself with a bit of thought.
Recently, however, I came across Model Context Protocol Servers – this is the first time in a while I was genuinely excited about the practical applications for this innovation.
Like any tool, AI (agents, assistants, etc.) are only as good as the data they have access to. When evaluating tools, this was always a limitation – the product looks great but is isolated from the information it needs to really become useful. In most cases, we’re trying to use cutting-edge technology but still relying on API access and custom integrations. This is the challenge Anthropic has attempted to meet with their release of the architectural concept of Model Context Protocol Servers (MCP) in November.
MCP is an open-source solution that bridges the gap between AI models and the data they rely on, removing the need for custom integrations and providing a standardised interface between AI assistants and external services.
In a traditional non-AI system, integrations between an external service are driven by SDKs and documentation (OpenAPI). These represent a static specification of a platform's capabilities. Contextually, MCP servers sit above that specification and provide an interface for AI models to understand and dynamically discover the tools, resources, and prompts available to interact with the service.
Now, rather than a developer reading documentation and understanding how to interface with an external service, they are able to use an MCP server to handle all of the implementation logic and simply apply the business logic through prompt engineering.
Away from application development, MCPs bring the same benefit to AI agent implementation and AI UIs like the Claude desktop app.
Potential benefits
The number of MCP servers available is limited to a few early adopters and is in no way ubiquitous. However, if it becomes widely adopted, there will be several benefits:
Streamlined deployment: MCP simplifies the process of integrating AI models with external services, reducing the time and effort required for deployment. Ideas can be realised faster with more opportunity to fail fast with little impact on project timelines or budgets.
Improved scalability: MCP allows AI models to scale more effectively, accommodating increased data and customer demand.
Greater flexibility: MCP provides a flexible framework for AI models to adapt to various tasks and environments, enhancing their overall utility and effectiveness without having to develop new integrations.
Improved performance: MCP will mean that AI models can be more accurately tailored to specific tasks, leading to improved performance and more reliable outcomes.
Challenges and limitations
Despite its potential, MCP faces several challenges and limitations:
Adoption: For MCP to be truly effective, it requires widespread adoption by AI developers and service providers.
Maintenance: MCP servers need to be created and maintained by services or enthusiasts, and like all open-source projects, can be resource-intensive and time-consuming.
Compatibility: There is a possibility that major AI players like OpenAI and GoogleAI may not adopt MCP, opting for their own solutions instead.
Future Outlook
The impact that MCP can have on our AI workflows and the services we can interact with is incredibly exciting. If adoption becomes widespread, MCP could lead to more accurate and reliable AI models, improved user experiences, and greater innovation in AI applications. As the technology evolves, it will be interesting to see how MCP shapes the landscape of AI and its integration with external services.