What Is Model Context Protocol (MCP), and Why Are Banks Asking for It?

Model Context Protocol (MCP) is an open standard for connecting large language models to external tools, data sources, and systems — a common interface instead of a custom integration for every connection. Anthropic released it in November 2024, and by mid-2026 it was already appearing as a named line item in enterprise RFPs for private AI platforms, including bank procurement documents that ask vendors to "support Model Context Protocol (MCP) to easily add new integrations."

That's a fast turnaround for a protocol to go from release to procurement requirement. Here's what MCP actually does, why it matters for a private or on-premise LLM deployment, and what to ask a vendor when it shows up in a requirements document.

What Model Context Protocol Actually Does

Before MCP, connecting an LLM to a new tool or data source — a database, a ticketing system, an internal API — usually meant writing a bespoke integration for that specific model and that specific tool. Every new combination was its own project. MCP replaces that with a standard client-server interface: an MCP server exposes a tool or data source in a consistent way, and any MCP-compatible model or application can talk to it without custom glue code.

The comparison people reach for is USB-C: before a common connector, every device needed its own cable. MCP aims to do the same for AI-to-tool connections — write the integration once as an MCP server, and any compliant model can use it.

Why It Shows Up in Bank RFPs Already

Regulated buyers evaluating a private LLM platform are rarely deploying it for a single, static use case. A bank's innovation team knows the first release will be a chat and document-intelligence tool, and that the second, third, and fourth releases will connect it to systems they haven't fully scoped yet — a core banking platform, an HR system, a CRM. Asking for MCP support up front is a way of buying architectural flexibility rather than a fixed feature set.

There's a second reason it's spreading fast: RFPs for this category are increasingly drafted with the help of an LLM. A buyer asking a model "what should I require in an RFP for a private enterprise AI platform" gets back a requirements list built from what the frontier of agentic AI tooling looks like right now — and MCP is currently the answer to "how do agents and models connect to tools." That's part of why a protocol released less than two years ago is already a named requirement, not a footnote.

What to Ask a Vendor When MCP Is on the Requirements List

"We support MCP" is a claim worth pressure-testing, not accepting at face value. A few questions separate a real answer from a roadmap promise:

• Can you connect an MCP server today, in a live demo, without custom development? If the answer requires an engineering sprint first, MCP support is roadmap, not shipped.
• Does MCP access respect the same permissions as everything else? A tool connection that bypasses role-based access control reintroduces the exact risk the rest of the platform is designed to prevent.
• Is every MCP call logged in the same audit trail as chat and retrieval? An integration layer that isn't captured in the audit database creates a blind spot in exactly the record a supervisor or examiner will ask for.

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Evaluating a private LLM platform and want a second read on the requirements list? Talk to a Sphere AI Engineer.