Answers
Does MCP make AI accurate on company data?
Partly. MCP gives a model live access to your systems, which genuinely improves reach and freshness. It does not create agreement between those systems: it has no concept of a resolved account key and no shared glossary, so identity and metric definitions are still inferred per query — and inferred differently each time.
Access and agreement are different problems
MCP is a protocol for letting a model reach tools and data. It is good at what it was designed for, and connecting an assistant to real systems is a genuine step up from pasting exports into a chat window.
Accuracy on business questions depends on something else: whether the systems being reached agree with each other. A protocol that transports a query cannot decide that two customer records are the same company, or which of three definitions of churn is the one your board uses. Those are organisational agreements, and they have to exist somewhere before a model can use them.
Where MCP genuinely helps
Reach. Connectors get to systems that were effectively invisible before, which is a real reduction in fragmentation rather than a cosmetic one.
Freshness. A live query against the source beats last night's batch, and it removes the class of error where an answer was quietly describing last week.
Unstructured data, partially. An assistant can read the Gong or Zendesk API directly. What it still cannot do is join those transcripts to the accounts they belong to, so the material is reachable without being analysable alongside the numbers.
Where it changes nothing
Entity resolution. MCP has no notion of a verified account key, so the model infers the join — usually on name or domain — and infers it afresh on every query. Subsidiaries, renamed accounts, and typos drop out silently.
Semantic consistency. With no shared glossary, a definition is invented per question. This is the mechanism behind the most common complaint about connected assistants: two people ask the same thing, get two different numbers, and both answers cite real data.
Neither gap is a flaw in MCP. They sit outside what a transport protocol is for, which is exactly why they need somewhere else to live.
What to put behind MCP
The missing pieces are a resolved entity layer, a governed glossary, monitored freshness, and unstructured sources joined to accounts. Put those behind the protocol and the same connection starts returning consistent answers, because the model is looking agreements up instead of reconstructing them.
This also makes the result portable. When the agreements live in a context layer rather than inside one assistant's configuration, swapping models or adding a second tool does not mean rebuilding the business logic — and the two tools agree with each other.
Keep MCP, add context
Riley is not an alternative to MCP and does not require giving it up. It speaks MCP, so Claude, Copilot, ChatGPT, and internal agents keep the connection they already have.
What changes is what is on the other end: a governed context layer that resolves entities, enforces definitions, keeps data current, and returns lineage with every answer, instead of a set of disconnected systems the model is left to reconcile on its own.
Related questions
Do I have to choose between MCP and a context layer?
No. They operate at different levels: MCP carries the request, and the context layer decides what the request resolves against. Riley is reached over MCP.
We already connected an assistant over MCP. What should we look at?
Ask the same question twice in two sessions, worded differently, and compare. Stable answers suggest identity and definitions are holding; differing answers point at entity resolution or missing definitions rather than anything in the model.