MCP Server

An MCP server (Model Context Protocol server) gives other AI tools and internal systems (a company’s own AI stack, or general-purpose assistants) programmatic, permission-scoped access to an application’s data.

What the protocol standardizes

Before MCP, every AI assistant that wanted to reach an internal system needed a bespoke integration written against that system’s API, with its own auth handling and its own description of what the tools do. MCP standardizes that layer: a server declares the resources and tools it exposes, and any compatible client can discover and call them without a custom adapter.

For a finance stack, that means one interface can serve several consumers: the company’s internal assistant, a developer’s script, an analytics agent. The same access rules apply to all of them, because the rules live in the server rather than in each client.

Why the governance question matters more here

Exposing financial data to an AI client raises two risks that a normal API integration doesn’t. The first is scope. An assistant that can query anything will, eventually, surface a salary line or an unclosed entity to someone who shouldn’t see it, so role scoping has to be enforced server side rather than requested politely in a prompt.

The second is that the client is a language model reading untrusted content. A document in the assistant’s context can carry prompt injection aimed at the tools the server exposes. The defense is to make the server’s write surface small and its destructive operations gated, so a hijacked client can’t do more than a hijacked read.

There’s a correctness dimension too. If the server returns raw numbers with no provenance, the consuming model reports them without citation and the whole verification chain ends at the boundary. A useful finance MCP server returns the figure and the evidence together.

What to look for

Read-only by default with writes explicitly enumerated. Permissions derived from the calling user’s role, not from a shared service account. Every returned figure carrying its source reference. And logging at the server, so there’s a record of what was asked and answered.

Rexfin’s MCP server exposes the governed model to a customer’s own AI tools under the same citation rules and role scoping as the interface itself: figures arrive reconciled, deterministically calculated, and cited. It’s developer and integration access, not a consumer feature.