Your OpenClaw, but Much More Efficient

General-purpose agents such as OpenClaw transmit their complete tool inventory, framework instructions, and interaction history to the model at every step, although each action typically depends on only a small subset of this context. We formalize this observation through the sufficient exposure of a request, defined as the least costly reduction of the full request that preserves task performance within a prescribed tolerance. Context beyond the sufficient exposure incurs two costs: redundant token consumption and distraction from irrelevant capabilities, which can degrade the model's decisions. Improving agent efficiency thus amounts to estimating the sufficient exposure online, a problem with asymmetric error costs: over-exposure incurs these costs gradually, whereas under-exposure withholds information required for correct execution. Motivated by this asymmetry, we propose LeanClaw, a request-level exposure controller for OpenClaw. LeanClaw initializes the exposed tool set from a compact prior over necessary tools and adapts it as execution feedback reveals new requirements or retires old ones. It extends the same criterion to interaction history through structured state preservation and duplicate removal, and it reverts to broader exposure whenever its estimate is uncertain. All runtime capabilities remain available; only the capabilities exposed in each request are altered. Across five benchmarks spanning mathematical reasoning, code generation, and reading comprehension, LeanClaw reduces token consumption by 46.64–59.29% relative to OpenClaw while improving task performance on every benchmark. On RealClawBench, it matches OpenClaw's success rate with 48.15% fewer tokens. Evaluations across multiple foundation models further demonstrate that these efficiency gains generalize beyond a single model route. Code is available at https://anonymous.4open.science/r/leanclaw-08BF.