Two Kinds of MCP

A pattern keeps recurring in our conversations with investment teams. A firm evaluates Orbit, likes what it sees, then pauses because it has just signed an MCP trial with a data vendor. When we compare notes, it usually turns out the two products share a name but little else.

The Model Context Protocol has quickly become the standard way to connect large language models to external systems, and nearly every financial data vendor now offers it. That is good news for the industry. It has also created a genuine buying hazard, because the same three letters now describe two products that solve different problems.

The data MCP

Most vendor MCPs are data MCPs. They provide your model with standardized access to a catalog of content: filings, transcripts, broker research, estimates, and prices. Think of them as pipes. When an analyst asks a question, the model pulls the relevant documents through the pipe, reads them, and assembles an answer.

The pipe is genuinely useful. It replaces brittle custom integrations with a common standard and puts high-quality content within reach of whichever model your firm runs. The reasoning, though, still belongs to you. Every query starts with raw text, and your model carries the full analytical burden each time.

The analytics MCP

The Orbit MCP works on a different principle. The reasoning happens inside the Orbit Insight platform before your model ever sees the result. Ask for guidance revisions across a peer group and what comes back is the finished analysis, structured and cited to source. Your model's job is presentation. The assembly already happened.

The simplest way to hold the distinction: a data MCP exposes nouns, meaning documents, fields, and prices. An analytics MCP exposes verbs, meaning analyze, compare, screen, and monitor.

Why the difference matters

Quality is the first consequence. A model working from raw text rereads and reinterprets the source on every run, so answers drift. Orbit reads from a persistent structured store, which makes results deterministic and auditable. The same question returns the same answer, and every claim traces back to a document. In our May 2026 benchmark, Claude paired with the Orbit MCP scored 6.51 against 5.62 for the same model working from raw material alone.

Cost and speed follow directly. Pushing full documents into a context window on every query is expensive and slow, and ten filings will crowd out everything else the analyst wanted the model to hold. Orbit does the heavy lifting once, then serves distilled results, so the context window stays free for the actual question.

Then there is the build. With a data MCP, the analysis layer on top remains your team's project to design and maintain. With an analytics MCP, that layer is the product.

Complementary by design

None of this is an argument against data trials. If your firm licenses content from an incumbent, an analytics MCP sits on top of it. If your AI team wants to build the analytical layer in-house, a data MCP is exactly the right foundation. The real decision is whether to build that layer or buy it, and it deserves to be made deliberately rather than settled by accident because two products happened to share a name.

Orbit Financial Technology is an award-winning AI investment research platform serving institutional investors worldwide. Orbit Insight underpins every step of investment decision-making, drawing on a Knowledge Base that processes more than 70 million documents annually across 75,000+ companies in 120 countries. These conversations matter deeply to us at Orbit. If you would like to see the analytics side for yourself, a trial takes minutes to set up. Get in touch.