Multimodal fake news proliferates on social media, yet its verification is hindered by context sparsity and analytical heterogeneity: deceptive posts often hinge on implicit social background, while distinct misinformation patterns demand different verification strategies. Existing methods rely on either representation-driven classifiers or LLM-based retrieval-augmented and tool-using pipelines, which act as opaque black boxes or follow rigid, predefined tool chains, failing to adapt reasoning to diverse news and yielding weakly grounded, uninterpretable decisions. To this end, we propose MCTL, a Meta-Cognitive Tool-Learning Framework that reformulates detection from static classification into in-context cognitive orchestration with dynamically planned tool-based reasoning. Specifically, MCTL retrieves cross-modal contextual evidence as external memory to compensate for context sparsity, performs meta-cognitive planning to construct a sample-specific Directed Acyclic Graph (DAG) reasoning blueprint, routes specialized tools along the DAG, and fuses conflicting judgments via consensus-aware arbitration, enabling context-grounded, sample-adaptive, and traceable verification. Extensive experiments demonstrate that MCTL consistently outperforms recent state-of-the-art agentic and RAG baselines, confirming its effectiveness and reliability.