Risk challenges
Enterprise application scenarios where AI intelligences may be missing key capabilities in specific scenarios (e.g., database operations).
MCPMark's Prevention Program
- scene preflighting: Stress testing in a Postgres/Notion environment actually used by the organization
- boundary test: Verify exception path handling capabilities with Filesystem tasks
- Stability verification: Set up multiple rounds of tests with K ≥ 5 to ensure that pass@K is met
Implementation of recommendations
- sandbox testing: Test high-risk operations (e.g. data writes) with an isolated environment first
- Progressive deployment: Hierarchical opening of permissions based on test results (e.g. read-only → read-write)
- Monitoring Optimization: Connect test metrics to the enterprise monitoring system to establish a baseline of capability
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 you avoid unanticipated capability deficiencies when deploying AI intelligences in your organization?
