Better LLMs cost 54% more tokens—because agents need context first

What happened

MCPMark V2 benchmarks showed that production LLMs achieve 54% higher token usage when reasoning through backend tasks, not because the models are less efficient but because they must discover system state that isn't handed to them cleanly. When an agent built a full-stack RAG app on Supabase, it consumed 10.4M tokens and required 10 manual interventions; the same app on InsForge (an agent-optimized open-source backend) consumed 3.7M tokens and completed without errors.

Why it matters

Most backends were designed for human developers using dashboards, so they return overly broad information (Supabase's OAuth docs include 5–10× more tokens than needed) and ambiguous error codes that force agents into retry loops. A smarter model tries harder to fill these gaps—triggering more discovery queries and reasoning—which is why token cost rises with a better Claude. The real fix is not a better model but a backend designed with agents in mind, offering structured context upfront and semantic exit codes.

What to watch

InsForge demonstrates the agent-first approach—a single CLI call returns the full backend topology in ~500 tokens before coding begins, with four narrowly scoped skills (CLI, debug, SDK, integrations) that activate only when needed. The GitHub repo has 14k+ stars. For teams building agent systems, this suggests that optimizing inference-time reasoning alone ignores the infrastructure gap: agents need backends that anticipate their information needs.