Disclosure: I work on Tura.
A typical MCP coding workflow may need five model re-entries:
- inspect files
- apply a patch
- build
- test
- lint
The commands still matter. What adds overhead is returning to the model after every step so it can read the output and make the next tool call.
Tura's command_run accepts a dependency-aware command graph. For predictable work, the agent can send the five-step plan once; the runtime still runs build, test, and lint, but the model goes from 5 turns to 1 in this example — an 80% reduction in model re-entries.
That is the idea behind the claim: less conversational/tool-calling overhead, not fewer engineering checks.
The task-level benchmark is here: https://turaai.net/benchmark-task?task=workflow-ecommerce-ad-package#runs
Source and setup: https://github.com/Tura-AI/tura
I’d be interested in how others are handling repeated tool-loop overhead in MCP agents.