Why your AI can answer questions but can't safely act, and what changes when it can
Your assistant can already summarize a report or draft an email. What it usually can't do is look up a live order in SAP, update a record, or run a real task in the systems that run your business. MCP is the open standard that closes that gap — one governed way for AI to read and act in your tools, instead of a custom integration for every app and every model.
MCP in one sentence
The Model Context Protocol (MCP) is an open standard that lets an AI client talk to your tools and data through one shared interface — so the model can do real work instead of only describing it.
Think of MCP as the USB-C port for AI. Before a universal port, every device needed its own cable and adapter. MCP is the single port that any compliant AI client can plug into, and any compliant tool can expose itself through — once, for everyone.
What it standardizes
MCP gives every AI client and every system the same vocabulary for three things:
- Tools — actions the AI can take (read an order, update a status).
- Resources — data and context the AI can read (a schema, a record set).
- Prompts — reusable task templates your team can trust and rerun.
Define them once and every MCP client — Claude, ChatGPT, Claude Code, Manus, your own agents — can use them without a rebuild.
What people did before MCP
Connecting AI to a real system used to mean hand-building the bridge yourself, and rebuilding it whenever something changed:
- Hand-rolled connectors — bespoke code per system, owned and patched forever by your team.
- Per-vendor plugins — one integration for ChatGPT, a different one for the next assistant, none of them portable.
- Raw function-calling glue — brittle wiring with no shared way to scope access, approve writes, or keep an audit trail.
The result was the same story everywhere: months of engineering per integration, a fresh rebuild every time a new model shipped, and no consistent answer to “who approved that change?”
What MCP unlocks for you
Because the interface is shared and open, you build the integration once and it lasts:
- Your AI moves from answering questions to completing real work.
- One governed setup serves every MCP client your team uses today and adopts next.
- Access, approvals, and audit live in one place instead of scattered across glue code.
Where DataTether fits
MCP is the standard. DataTether is the governed layer that gives you that standard for your ERP without building or running a server. Connect an OData service once and it auto-generates the tools, resources, and approval gates — so the only thing your team brings is the question. See how the pieces fit together →