Data API vs MCP: When to Use Each
Two ways to consume, two use cases and a decision guide.
- Understand what each one is
- Choose by use case
- Combine the two when it makes sense
Once you have data in your workspace, the practical question arises: how consume them through my application or agents? Nekt gives you two paths, the Data API and the MCP. They don't compete, they solve problems different, and they both consume the same data, from where they are. In practice, the vast majority of customers consume directly of the raw data (Bronze) and that It already works well. Understanding the difference between the two paths is what avoid using the wrong tool for the right job.
The two paths
Data API. It is the programmatic and deterministic access to your tables, either directly from the raw data in Bronze or tables already modelled. You write a request, it returns exactly the same given, in the same form, every time. Ideal for apps, dashboards and automations that always need the same number, in a predictable way and with low latency. You know exactly which query runs and what back.
MCP. It is the interface for AI agents to explore and answer questions in natural language about your data, wherever that they are. Instead of writing the query, you (or your team) asks "how much did we sell per channel last month" and the agent find out how to respond. Ideal for exploration, business questions open and for non-technical teams. It depends less on having everything modeled and more of the context, the Semantic Layer with descriptions and documents that explain the data, to answer well.
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"uso_mes": 1842,
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Deterministic vs Exploratory
This is the fundamental difference. The Data API is deterministic: same input, same output, always. That's what you want when a system depends on the result, a graph that cannot vary, an automation which triggers based on a number.
MCP is exploratory: the question is open, the path to the answer is decided by the agent on the spot. It's what you want when the question is not yet known, when the person consulting is a person investigating, not a running system.
MCP is only as good as the context behind it. One agent connected to data without any description will guess and miss, even if they are well modeled. Connected to the same data, but with the Semantic Layer describing metrics, columns and rules, it responds accurately. What moves the assertiveness needle the most? It's not modeling everything, it's giving context. If the MCP is responding poorly, the The problem is almost always in the missing context, not in the layer where the data is.
Start consuming directly, improve with context, consolidate when repeat. You don't need to model everything before generating value. Consume the data wherever it is, give context for the AI to understand, and only consolidate a query into a table (Silver or Gold) when she starts to repeat a lot and be worth optimizing. Optimization comes after value, not before.
Comparing the two
| Dimension | Data API | MCP |
|---|---|---|
| Interface | Programmatic request (HTTP) | Natural language via AI agent |
| Best for | Apps, dashboards and automations | Exploration and open-ended business questions |
| Determinism | Deterministic: same input, same output | Exploratory: the path is decided on the spot |
| Latency | Low and predictable | Variable, depends on the question |
| Public | Systems and technical teams | People, including non-technical teams |
| Depends on | The right table exposed, from any layer | Good context (Semantic Layer); to Gold helps, but it's not mandatory |
It's not one or the other
In practice, many companies use both at the same time, and over the same base. The production app pulls metrics via Data API, so predictable. The business team asks via MCP agent, so exploratory. Both consume the same data, the same source of true, whether they are raw or already modeled. One data, two ways of consumption.
A SaaS displays an "account usage" panel in the product itself to
each customer. This number cannot vary or depend on
interpretation: it pulls the table via Data API
gold.uso_por_conta, with a fixed and low request
latency. Every time the screen loads, it runs the same query and
returns the same data. Predictable, fast, reliable.
Commercial leadership wants to understand why the funnel fell last week. No one knows in advance which query answers this. fur MCP agent, someone asks in natural language "compare the conversion by origin between this week and the previous one", the agent reads the Semantic Layer, finds the right tables and responds. In sequence, "and by seller?". Open exploration, without writing a SQL line.