Understand MCP in 5 Mins: What’s Missing in PE’s Current AI Strategy?

From Public to Proprietary Data: How PE Firms Are Using AI Today

Walk into any private equity office today and you’ll see AI language models like ChatGPT everywhere: mapping market trends, summarizing CIMs, and proofreading emails. It feels a lot like the early days of Microsoft Office, everyone’s figuring out what works as they go.

But one critical challenge remains for private equity investors: unlocking the full potential of AI by deriving insights from a firm’s internal knowledge and collective experience.

That’s why the concept of Model Context Protocol (MCP) has generated so much attention.

What is Model Context Protocol (MCP)?

✅ MCP is a connector that lets AI tap directly into your external systems.

✅ In private equity use cases, it gives AI on-demand access to context from CRMs like DealCloud and Salesforce, file-sharing services like Sharepoint or Egnyte, and beyond.

❌ MCP doesn’t interpret or structure the data.

Like any new tool, however, it’s effective in some scenarios but less applicable in others.

In this post, we’ll break down what MCP is, highlight where it works best, and help you understand how it might fit into your firm’s broader AI strategy.

The Librarian Metaphor: Why ChatGPT + MCP Aren't Enough

Think of LLMs like ChatGPT as a smart librarian, and MCP servers are the librarian’s access badges.

Each badge can fetch books from any authorized shelf, even several at once. However, the reading and cross-referencing is done by the AI model.

For example, a PE associate might ask ChatGPT:“Give me an update on Company A.”The librarian could then make three separate trips: (1) to the Salesforce (CRM) shelf, (2) the PitchBook shelf, and (3) the SharePoint shelf.

LLM + MCP Integration: PE Deal Team Use Cases

Here’s the problem:

1. Insight quality depends on prompt quality:Each shelf operates independently, so seamlessly stitching notes from each trip isn’t guaranteed. Without clear prompts, MCP may hit only one source and return shallow results.
2. Insights stay siloed with individuals:ChatGPT isn't a shared system of record, it's a query processor that is not built to preserve context across your team. When your colleagues need access to team insights, they start from zero again.

The pain isn’t only fragmented information, but the lack of shared institutional memory, so the disconnection persists across teams.

The Real Limitations PE Leaders Should Know

This is the key concept private equity leaders should understand: MCP connects tools, but it doesn’t connect contexts.To build a true system of record that captures your firm’s investment history, institutional expertise, and hard-won lessons, you need a platform explicitly designed to support that workflow.

So when considering MCP, here are the limitations to be aware of:

1) MCP Operates in Data Silos

MCP can pull data from multiple sources, but it doesn’t unify or connect insights across them. To reliably answer decision-making questions, you still need powerful processing, structuring, and storage in a consolidated system of record. Here’s an example of a query MCP alone would struggle with:

“What are the last three HVAC deals where customer concentration was flagged as a risk, and has this same risk caused any recent deals, as noted in a recent IC memo?”

2) Surface-Level Intelligence

MCP does not process data itself, so it cannot interpret or enrich the information it retrieves. When you pull a prior IC memo from Egnyte, MCP delivers the document, but the task of identifying risks still falls to the AI.

3) No System of Record

MCP is an access layer, not a unified system of record across your firm’s history. Past investment insights, scoring frameworks, and value-creation strategies stay siloed, so its impact is limited to individual users rather than the fund as a whole.

This is not to say MCP cannot provide value. Quite the opposite, there are strong use cases it can support. Email is a good example: since message bodies are retrievable, the AI can search and summarize full correspondence, not just metadata.

What PE Actually Needs: An Institutional Library Designed to Unlock Insights at Scale

Given these limitations, transforming data into consolidated institutional memory requires a separate layer of dedicated infrastructure.

So let’s imagine your librarian got a serious upgrade. They can access every shelf at once, have read every page, and can connect the dots across all the books.

Unifying Data Silos into A Living Institutional Library

This master librarian doesn’t just know where Company A’s data lives. It also spots that Company B, a past portfolio company in the same subsector, is a relevant comparable. It sees that Company A’s revenue mirrors Company B’s trajectory, which thrived until market conditions shifted. And it remembers that in 2020, the real risk wasn’t market size but the management team’s ability to handle supply chain disruptions.

Because the librarian understands the contents of each document, it can highlight relationships across sources and connect key data points. Even more, it can spot patterns from past analyses and contextualize every new piece of knowledge that enters the firm.

Metal as Your Master Librarian

What Winning PE Firms Are Building with Metal

The smartest PE leaders aren’t asking: “How do we connect AI to our tools?”

They’re asking: “How do we use AI to make our firm fundamentally better at investing?”

Metal is building what PE firms have always needed but never had: unified institutional intelligence that captures your investment philosophy, decision patterns, and portfolio outcomes. What was once scattered across individual minds and siloed systems becomes a living, evolving firm memory which is accessible to every deal team, on every deal.

AI adoption in PE is at an inflection point. Firms that treat AI as just a faster memo writer or a smarter search bar will end up with costly tools that provide only temporary efficiency, not lasting advantage. MCP and LLM (ChatGPT/Claude/Gemini, etc) integrations are the ticket to play, not the way to win.

The real advantage will come from firms that use AI to capture, connect, and compound their investment expertise. These firms will build moats that grow wider with every deal.

Learn more about how Metal can build your institutional advantage.