Background and current status of the issue
Currently, there are two major challenges in assessing the capability of big models as intelligences: one is the lack of unified standards, and the other is that the test environment is detached from real scenarios.MCPMark can fundamentally solve this problem by providing a standardized test framework and a real software integration environment.
Core Solutions
- Environmental standardization: Integrate six real tool environments (Notion/GitHub, etc.) to ensure that test scenarios are consistent with business scenarios
- Harmonization of indicators: Provide four aggregation metrics such as pass@1/pass@K to eliminate subjective differences in assessment results
- process automation: each task with validation scripts, support for failure automatically renewed to ensure that the results can be reproduced
Operation Guide
1. Rapid deployment of environments via Docker or Pip
2. Configure the .mcp_env file to connect to the measurement model APIs
3. Run test tasks using the command line (full/group testing support)
4. Generation of standardized reports in CSV/JSON format
This answer comes from the articleMCPMark: Benchmarking the Ability of Large Model-Integrated MCPs to Perform Intelligent Body TasksThe
May not be reproduced without permission:AI productivity tools " How can MCPMark be used to address the problem of non-standardized assessment of the capabilities of large model intelligences?
