MCP and the Emerging Standard for AI-Ready Financial Data

As artificial intelligence moves from experimentation into real-world deployment, one challenge has become increasingly clear across the financial industry: how AI systems access, interpret, and reason over trusted market data. 

In recent months, Model Context Protocol (MCP) has emerged as a potential answer to this problem. With growing support from AI developers and data providers alike, MCP is drawing attention as a possible foundation for AI-native financial data infrastructure. 

From Experimental AI to Production Constraints 

Early enterprise AI initiatives often rely on static datasets, manual exports, or heavily customized data pipelines. While sufficient for prototyping, these approaches have struggled to scale into production environments where accuracy, governance, and timeliness are critical. 

As firms attempt to operationalize AI for research, risk management, and strategic analysis, traditional data architectures increasingly appear misaligned with real-time, model-driven workflows. The result has been rising interest in standardized mechanisms that allow AI systems to interact directly with authoritative data sources. 

What MCP Represents 

Model Context Protocol is designed to standardize how large language models and enterprise AI systems connect to external data environments. Rather than embedding data into models or maintaining redundant warehouses, MCP enables models to query external datasets directly through a governed interface. 

The recent decision by AI company Anthropic to release MCP as open source has accelerated industry discussion around standardization. By making the protocol publicly available, MCP can be adopted, modified, and implemented by a wide range of organizations, increasing the likelihood of convergence around a shared approach. 

This has shifted MCP from a proprietary experiment into a broader industry reference point—though it remains an evolving standard rather than a finalized one. 

Data Vendors Begin to Respond 

As MCP gains visibility, financial data providers are beginning to explore how such protocols might fit into their existing delivery models. One recent example is FactSet, which announced the availability of a production-grade MCP server enabling AI systems to access selected financial datasets directly. 

FactSet’s implementation provides access to multiple categories of market intelligence, including company fundamentals, pricing data, ownership information, M&A activity, and supply chain insights. The company has positioned this offering as an extension of its existing API infrastructure, adapted for AI and agent-based applications. 

While this represents one of the earliest large-scale deployments of MCP within financial data services, it is best viewed as an early signal rather than a settled industry outcome. Other vendors are expected to explore similar approaches, whether through MCP or alternative standards. 

Opportunities and Open Questions 

The appeal of MCP-style architecture is clear. Direct AI-to-data access has the potential to: 

• Reduce operational complexity 

• Improve timeliness of analysis 

• Minimize data duplication 

• Enable more dynamic, conversational use of financial intelligence 

At the same time, several open questions remain unresolved. MCP adoption is still in its early stages, and long-term success will depend on factors such as: 

• Industry-wide agreement on standards 

• Interoperability across AI platforms 

• Governance, permissioning, and auditability 

• Vendor willingness to support non-proprietary access models 

Additionally, it is not yet clear whether MCP will emerge as the dominant standard or coexist alongside other approaches to AI-data integration. 

Implications for the Financial Industry 

For financial institutions and data teams, the rise of MCP highlights a broader shift: data delivery is becoming as important as data content. As AI systems increasingly act as primary interfaces for analysis and decision-making, the ability to serve data in AI-native formats may become a baseline expectation rather than a competitive differentiator. 

This transition suggests that future competition among data vendors may focus less on standalone platforms and more on how seamlessly data integrates into AI-driven workflows—regardless of the underlying model or provider. 

Conclusion 

MCP reflects a growing recognition that AI requires new assumptions about how data is accessed and used. While still early, the protocol has sparked meaningful discussion across AI developers, data providers, and enterprise users about the future of financial data infrastructure. 

Whether MCP ultimately becomes a dominant standard remains uncertain. What is increasingly clear, however, is that AI-ready data access is moving from a niche experiment to a core requirement, reshaping how the financial industry thinks about data, systems, and decision-making. 

References  

• FactSet Press Release: FactSet Meets Demand for AI-Ready Data, First to Announce MCP Sans Intermediary https://investor.factset.com/news-releases/news-release-details/factset-meets-demand-ai-ready-data-first-announce-mcp-sans 

• FactSet AI Overview https://www.factset.com/ai 

• FactSet Developer Platform https://developer.factset.com 

• FactSet Insight: Enterprise MCP – Model Context Protocol https://insight.factset.com/enterprise-mcp-model-context-protocol-part-one