James Perkins
Despite rapid advances in artificial intelligence (AI), many financial services teams still face challenges when deploying models in real-world workflows. A key limitation is that models may not consistently access governed, real-time data in a consistent or reliable manner across all use cases.
While AI capabilities continue to expand, certain tools may still have constrained access to real-time, structured, and trusted data, factors that can influence performance in practical use cases. In some scenarios, models operate with restricted context and may not connect directly to external systems, which can, in some cases, contribute to a gap between potential and real-world applications.
The Model Context Protocol (MCP) is one approach being explored to support more standardised AI integration. By aiming to enable more consistent connections with external data sources and tools, frameworks like MCP aim to streamline access to data across APIs and systems. Compared with highly customised integrations, a more unified approach may help simplify how data is accessed and used.
What is MCP?
MCP is an emerging open standard designed to support AI systems in connecting with external data, tools, and services, helping them access the context needed to support more context-informed outputs.
MCP provides a common framework that aims to standardise how different systems interact. Instead of relying on complex, one-off integrations, it offers a more consistent way for AI to request and receive information.
At a high level, it works through a simple structure: an AI application (host) connects via a standard interface (connector) to external data sources or tools (servers), supporting a more scalable and consistent approach to integration.
How does MCP work?
In practice, MCP can enable AI systems to go beyond static, pre-trained responses by allowing them to retrieve real-time data, perform calculations, and interact with external tools. This can, in some cases, support a shift from AI simply providing answers to supporting more actionable and dynamic workflows. Through MCP, AI can access three key elements:
- Tools - the actions MCP allows an AI system to take
- Data - the information MCP enables it to retrieve
- Context - how MCP can support the model in interpreting and responding to a request
Together, these capabilities can help support more relevant and context-informed AI outputs.
For example, an AI assistant used by a financial services team could use MCP to query a governed market dataset, retrieve reference data, apply a calculation, and return an answer that can reflect current market conditions. Instead of relying on static training data or manual inputs, the model can access trusted, up-to-date information when available.
Why MCP matters for AI in financial services
In financial services, where decisions depend on accurate, structured, and well-governed data, the ability to connect AI with reliable information sources is increasingly important. This is particularly relevant in environments such as LSEG, where access to trusted, structured financial data is critical.
MCP supports:
- More consistent access to structured and trusted data for regulated decision-making
- Improved context accuracy for AI-driven outputs
- More efficient and streamlined workflows
By providing a standardised way for AI systems to interact with data and tools, protocols like MCP aim to help organisations deliver information in a format that AI can use more consistently. Over time, this can support the development of AI systems that can work more directly with real-world financial data, enabling more informed and context-aware analysis.
Key takeaways
- Many AI systems remain limited by data access and integration complexity
- MCP serves as a standardised approach to connecting AI with data and tools
- MCP supports more context-aware and data-driven AI applications
Further learning on MCP
For readers who want to explore this topic in more depth, the LSEG Academy learning path on Model Context Protocol (MCP) looks more closely at how MCP supports context-aware AI, real-time data access, and data-driven decision-making across industries.
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