From dialogue to discipline

Fragmentation constrains how AI can interact with your data. If you want a complete picture of anything from intraday risk to quarterly P&L, the value of bringing together multiple data points into a conversational response to a chat query depends on which data layers you are touching.

The challenge goes beyond truisms about data readiness and extends deep into firms’ approach to data governance. For data about assets and transactions, definitional consistency is essential. Firms need:

Common identifiers and taxonomies across portfolios and entities
Semantic alignment between risk, performance, and accounting data
Lineage and governance that explain every change made
Access and entitlement controls that match users’ fiduciary responsibilities

Without those disciplines, people can’t be certain what MCP-enabled outputs mean or whether they are valid. They also set the stakes for when to MCP and when not to.

The stakes may not be as tragic as Hamlet’s existential angst of whether to be or not to be. But getting the choice between whether to MCP or not to MCP wrong can have unintended consequences. Luckily, teams building investment technology can rely on some valuable rules of thumb to answer the question.

When to explore

In general, using MCP makes sense for lightweight, distributed access to unstructured data, such as knowledge systems, documents, and communications. Imagine a repository of deal documents or trustee notices for a pool of loans, for example. With the right MCP server, AI can enable users to explore and consider context without re-platforming.

When you’re connecting to unstructured or semi-structured sources, MCP servers can accelerate development. Secure, temporary access to data can take place without deep system integration. The use cases tend to be ad-hoc data or context retrieval, e.g., ontology search, cross-referencing, or workflow enrichment. However, teams should monitor context window consumption as connected MCP servers scale — loading numerous tool definitions and passing large intermediate results can quickly increase latency and costs,ⁱ particularly when orchestrating across dozens of data providers in capital markets workflows.

For example, asking an AI to “list differences in all OTC confirmations from Q3 2024” would benefit from an MCP server that lets users search context (which asset classes, where is data located) and retrieve trade confirmation data (document repository or email attachments). MCP's value amplifies when workflows require coordination across multiple data sources, rather than querying a single governed system. When done well, it saves operations teams tangible effort and represents the “right” way to leverage MCP, focusing on narrow data points instead of massive transformation projects.

When to integrate

However, curated data integration can be a better approach for centralized, modeled, governed data, optimized for consistency, traceability, regulatory assurance, and analysis response time. For example, books and records, i.e., historical datasets that contain the official record of fiduciary, financial, or risk-related activities, would benefit from centralized data services.

When you need regulatory-grade inputs and outputs that you can reconcile and audit, or when you need mathematical precision across systems, platform-led, tightly integrated systems are critical for business.

Similarly, if you have legacy applications without modern endpoints, MCP is not designed to handle consuming their data or functionality without additional complex implementation. For example, responding to a regulatory inquiry about fee discrepancies that require data from your legacy fund structure would push MCP against pragmatically unsolvable issues such as incomplete data models, undocumented databases, or obsolete file formats, which could result in incomplete or misleading regulatory responses that create compliance risk.

Not to be, or not only

Unlike Hamlet, you have the luxury of taking a hybrid approach without feeling haunted. Understanding MCP’s value depends on how you plan to use it. The goal of today’s data leader is to balance semantic control and conversational access.

While MCP will likely become a standard expectation for vendors and platforms, the future lies in using MCP’s flexibility alongside robust platform-level governance.

Rather than bypassing the walls that naturally build up in investment data over time, MCP makes the most sense as part of the ongoing discipline of governing and using data from front to back office and beyond.