Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value. Existing evaluations typically report performance metrics, but rarely examine agentic viability: whether dynamic LLM-mediated decisions convert their induced costs into measurable incremental profit. To apply this criterion, we introduce TradeLens, a trace-grounded diagnostic toolkit for evaluating agentic trading systems from their trading records, runtime traces, and deployment configurations. It reconstructs trading trajectories, attributes profit and cost to interpretable evidence, and diagnoses whether and why an agent pays for its own intelligence. We conduct extensive analysis across backbone models, capital scales, trading frequencies, and system architectures, together with deployment discussion. Our results show that viability hinges on intelligence-to-profit conversion: models exhibit different failure patterns, such as poor asset selection in DeepSeek-V3.2 and negative timing in GLM-4.7, while capital scale, trading frequency, and architecture matter primarily in these runs, especially through their effects on decision-attributed timing value. These findings reframe the evaluation of LLM-based trading agents from capability-centric performance ranking to trace-grounded diagnosis of intelligence-to-profit conversion. Our code is available at https://github.com/ParadooxAI/TradeLens.

Standardized interfaces such as the Model Context Protocol (MCP) make it easier to connect tools to agents, but connection alone is not orchestration. When many related tools are exposed as a flat callable palette, agents must identify relevant tools, resolve substitutes, pass intermediate outputs, and decide when a workflow is complete. We introduce RealFin, a finance-domain diagnostic benchmark for evaluating workflow construction under MCP-style flat tool exposure. RealFin contains 1524 tasks generated from 108 author-designed financial workflow templates over real market, macroeconomic, and company-data APIs. Each task is paired with an executable reference program that specifies one verified solution path, while the 85 agent-callable tools include explicit alternative, dependency, and related-tool metadata to represent non-unique paths, prerequisite tools, and near-neighbor ambiguity. This design supports path-independent answer scoring together with process-level evaluation of tool selection, ordering, parameter planning, entity grounding, temporal grounding, orchestration success, and error recovery. Across ReAct-style flat exposure, retrieval-based exposure, oracle-controlled variants, and progressive tool disclosure, current agents often achieve nonzero answer accuracy while failing to construct reliable workflows. These results suggest that MCP-style tool connectivity needs structured disclosure and workflow-aware evaluation, especially in finance where temporal constraints and overlapping tools are intrinsic.