Question 1

What is the difference between a native MCP server and a task-specific MCP server?

Accepted Answer

A native MCP server wraps a vendor's API and hands the agent the whole surface, so the agent decides which account, which fields and which query to use. A task-specific MCP server exposes a small set of pre-modeled data products for a defined job, so the agent makes one call against one contract. Both can return identical data.

Question 2

Why did the native Google Ads MCP server use more tool calls?

Accepted Answer

Its contract makes the agent discover context first. In every task-cell the agent listed accessible accounts before doing anything, and often looked up the schema next. Those discovery calls are structural, so they repeat on every task.

Question 3

Why do fewer tool calls mean fewer tokens?

Accepted Answer

Input tokens get billed on every turn, and each turn resends the system prompt, all tool schemas and the accumulated context. A four-call task pays that overhead four times. Cutting turns cuts tokens faster than cutting payload size does.

Question 4

Is the token difference specific to one model?

Accepted Answer

No. The task-specific server used 4.7× fewer tokens with claude-opus-5 and 6.8× fewer with gpt-5.5, and made one call per task in both.

Question 5

Did the task-specific server return the same data as the Google Ads API?

Accepted Answer

Yes. We derived ground truth directly from the Google Ads API with GAQL and checked it against all six Nexla tools. They agreed at every level, so pre-modeling the data cost nothing in fidelity.

Question 6

How long does each server take to set up?

Accepted Answer

The task-specific server installed with one config block, a URL and a bearer token. The native path took us two working sessions across two days, most of it waiting on a developer token and clicking through Cloud Console to create an OAuth client.

Nexla Blog

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Nexla MCP Server vs. Google Ads MCP: What a Task-Specific vs. Native Benchmark Actually Showed

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