Cutting AI Token Costs When Querying Amazon Data

Teams that connect an assistant to their Amazon data usually discover the cost problem in week two. The fix is rarely a cheaper model. It is changing what comes back from the server, and the same change makes the answers more accurate.

Where the tokens actually go

An MCP tool call has three cost components: the tool schemas the model reads to choose a tool, the arguments it sends, and the payload that comes back. The third one dominates, by a wide margin, and it has a compounding property people miss: the result stays in the conversation. Ask five follow-up questions and you pay for that payload five more times as input context.

Now consider what "get my sales data" means against raw marketplace APIs. Orders are one endpoint, financial events another, advertising a third. Each returns nested JSON with dozens of fields per record, most of which nobody asked for. Amazon also throttles per operation, documented in the SP-API usage plans and rate limits reference, so the assistant is not only paying tokens, it is waiting between pages.

The same question, two architectures

Take a routine question: "which search terms wasted more than $100 last month with no sales?"

The difference is not the protocol and not the model. It is whether the filter ran before or after the data crossed the wire.

Six practical rules

1. Push thresholds into the call. "Spend above $100 and zero orders" should be arguments, not a post-filter the model applies.
2. Ask for a ranked top N. You almost never need row 200. Limit every exploratory question to 10 or 20 rows.
3. Prefer computed metrics over components. Request contribution margin, not the twelve fee lines that build it, unless the fee split is the question.
4. Group at the right grain. A monthly series should be one call with a month grain, never twelve calls, and never daily rows the model rolls up itself.
5. Start new conversations for new topics. Context carries. A fresh thread drops the payload you no longer need.
6. Keep the tool surface small and well described. Twelve precise tools beat two hundred thin ones for both cost and tool-selection accuracy.

What a well-designed server does for you

The protocol itself is neutral on all of this. The MCP specification describes how tools are discovered and called; it says nothing about how much you should return. That is the vendor's choice, and it is the choice worth interrogating before you connect anything.

Concretely, look for:

• Metric filters as first-class arguments, so thresholds run in the database.
• Sorting and limits on every list-returning tool.
• Time grains, so a twelve-month series is one call.
• A metric catalog the assistant can read once instead of guessing field names.
• Comparison periods built in, so variance is computed rather than derived in the prompt.

Nova MCP is built to those rules, which is why a working session usually costs less than people expect. The Amazon MCP hub lists the tools and their filters, and the prompt library shows the phrasing that keeps payloads small.