Only 52 out of 56 tasks were successful.

[WEYAI]

2025-09-29 07:22:59,740 - benchmark.results_aggregator - WARNING - Skipping result without evaluation for task google_maps_000
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO -

2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Cumulative Metrics for gpt-5 (mcpbench_tasks_single_runner_format.json) (52/56 tasks completed):
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - ================================================================================
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - LLM Judge Scores:
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Task Completion Score: 8.436
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Tool Selection Score: 8.673
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Planning Effectiveness Score: 8.065
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Subdimension Scores:
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Task: Fulfillment 8.371, Grounding 8.501
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Tool: Appropriateness 8.877, Parameter Accuracy 8.469
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Plan: Dependency 8.388, Efficiency 7.743
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Tool Accuracy Metrics:
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Input Schema Compliance: 99.68% (among valid tool names)
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Valid Tool Name Rate: 100.00%
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Tool Call Success Rate: 98.88%
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - Execution Statistics:
2025-09-29 07:22:59,741 - benchmark.results_formatter - INFO - • Avg Rounds per Task: 7.36
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - • Avg Tool Calls per Task: 59.84
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - Token Usage (OpenAI Tokenizer):
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - • Avg Output Tokens per Task: 37738
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - • Avg Prompt Tokens per Task: 386392
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - • Avg Total Tokens per Task: 424130
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - Runtime:
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - • Avg Agent Execution Time: 1328.5s
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - • Avg Judge Pipeline Time: 74.4s
2025-09-29 07:22:59,742 - benchmark.results_formatter - INFO - ================================================================================

Model gpt-5: 100%|████████████████████████████████████████████████████████████████████████████████████████| 56/56 [37:09:06<00:00, 2388.33s/it]
2025-09-29 07:23:00,746 - benchmark.runner - INFO - Model gpt-5 completed: 52/56 tasks successful
2025-09-29 07:23:00,746 - benchmark.runner - INFO - MCP-Bench benchmark completed: 1 models tested
2025-09-29 07:23:00,781 - benchmark.runner - INFO - Results saved to benchmark_results_20250929_072300.json
2025-09-29 07:23:00,781 - benchmark.runner - INFO - The overall score is calculated as the average of four main dimensions: schema understanding, task completion, tool usage, and planning effectiveness. Within each dimension (e.g., schema understanding), we first compute the mean across its sub-dimensions.

Only 52 out of 56 tasks were successful. Will this affect the final result? If so, will it increase or decrease his score?

[WEYAI]

If we can't guarantee 100% task success, comparing these results with the table in the paper might be unfair. For example, if a single service in a task has 52 successful operations out of 56, should the weight be calculated as 56/104 or 52/100?

[ZhentingWang]

Hi, one possible reason for the failed execution is that the task may not have completed within the timeout threshold. Could you please check the log file to confirm if this was the case? If so, you might consider increasing the value of task_timeout at line 33 in ./config/benchmark_config.yaml.

[WEYAI]

If there is no way to increase the completion rate of the task, is it fair to directly compare the results with those of the paper's experiments to see whether the effect has improved or declined?
如果无法提高任务的完成率，那么直接将结果与论文中的实验结果进行比较，效果是提升还是下降，这种方式判断公平吗？

[WEYAI]

I understand that currently the calculation of points is based on the average score of each sub-dimension. If 52 tasks are successfully completed, then it would be the average score of those 52 tasks. However, this is not fair compared to 56 tasks. There is no way to say whether this increases or decreases his effectiveness because both possibilities exist.
我理解目前的评分方式是根据每个子维度的平均得分来计算的。如果完成了 52 项任务，那么就是这 52 项任务的平均得分。然而，与完成 56 项任务相比，这种方式就不公平了。我们无法确定这样做是会提高还是降低他的效果，因为这两种情况都有可能发生。不知道这种理解对不对。