Mobile planning requires a central agent to coordinate tools, persistent user context, reusable procedures, and specialized sub-agents while adapting to execution feedback. Evaluating these workflows requires distinguishing task completion from compliance with prescribed resource and procedural requirements. We introduce \textbf{MobilePA-Bench}, a benchmark comprising 1,705 tasks across eight query task domains and 212 mobile tools in a stateful simulated environment. It combines three features: (1) unified capability coverage, profiling Tool Use, Memory Usage, Skill Usage, and Sub-agent Collaboration under a common mobile protocol; (2) evidence-aligned verification, matching each task to a fixed tool-call, state-change, or interaction-behavior checker rather than imposing one completion criterion on all tasks; and (3) capability-gated evaluation, requiring task completion alongside prescribed memory checks and, in skill-only routing, complete route loading before business-tool use. With MobilePA-Bench, we systematically evaluate open- and closed-weight LLMs, revealing capability-specific weaknesses in complex mobile task execution. Fine-grained analyses distinguish failures in resource retrieval and procedural compliance from downstream execution errors, providing diagnostic insights beyond aggregate task-success rates. We further train a Qwen3.8-27B baseline with online reinforcement learning in the sandbox, whose improved performance provides initial evidence of the environment's utility for planner development.

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