As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their action spaces offers agents unsafe capabilities and underscores the risk of power-seeking. While broad action space and greater environment influence are essential for task fulfillment, they creates a fragile risk surface where minor errors or hallucinations are magnified into catastrophic failures. In response, we propose SafeMCP, a server-side defense plugin that constrains tool acquisition via predictive reasoning regarding future safety risks. SafeMCP utilizes an internal world model for look-ahead reasoning to implement a two-tier defense: proactive tool filtering to constrain hazardous power expansion and immediate intervention as a fail-safe. To train SafeMCP, we introduce a three-stage pipeline comprising environmental dynamic grounding, safe policy initialization, and reinforcement learning (RL) with dual verifiable rewards. Experiments on PowerSeeking Bench, ToolEmu, and AgentHarm show that SafeMCP achieves a safe equilibrium, effectively mitigating risks while preserving agent utility.

Open-ended event forecasting (OEEF) seeks to predict future events from a given context without being restricted to a predefined scope or format. It plays a crucial role in domains such as risk management and financial decision making. Although large language models show potential for OEEF, existing approaches and datasets often overlook the complex relationships among events, and current research lacks comprehensive evaluation methods. To address these limitations, we propose ForestCast, a prediction pipeline that extracts forecast-relevant events from news data, organizes them into a story tree, and predicts subsequent events along each path. The pipeline comprises four stages: (1) grouping news into event nodes, (2) constructing a news story tree, (3) mining the semantic structure of the tree, and (4) predicting the next event node and evaluating prediction quality. To support this pipeline, we construct NewsForest, a dataset of 12,406 event chains, each representing a chronologically and logically linked sequence of news events. In addition, we introduce a comprehensive evaluation framework that measures both the accuracy and the quality of prediction. Experimental results demonstrate that ForestCast improves the ability of LLMs to forecast events in news data.