MCPMark Assessment Process Explained
Model evaluation using MCPMark typically involves four key steps:
1. Preparation for installation
Complete the tool installation and environment configuration according to the previous description
2. Authorization of services
Configure API access for services to be tested (GitHub/Notion etc.)
3. Operational assessment
- Full volume testing:
python -m pipeline --exp-name 实验名 --mcp 环境 --tasks all --models 模型名 --k 尝试次数 - Group testing: Specific task groups such as online_resume can be specified.
4. Analysis of results
- The raw results are saved in the
./results/catalogs - Use the aggregation command to generate reports:
python -m src.aggregators.aggregate_results --exp-name 实验名
Detailed reports in JSON and CSV formats are generated for each experiment, supporting multi-dimensional analysis of multiple metrics.
This answer comes from the articleMCPMark: Benchmarking the Ability of Large Model-Integrated MCPs to Perform Intelligent Body TasksThe
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