agents benchmark
MCP-Mark evaluates LLMs on their ability to use Model Context Protocol (MCP) tools effectively, testing tool discovery, selection, invocation, and result interpretation across diverse MCP server scenarios.
Updated Aug 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | MA | 81.1% | 100.0% | 8 | C | |
| 2 | AC | 60.8% | 85.7% | 8 | C | |
| 3 | AC | 58.7% | 71.4% | 8 | C | |
| 4 | MA | 55.9% | 57.1% | 8 | C | |
| 5 | AC | 48.2% | 42.9% | 8 | C | |
| 6 | AC | 46.1% | 28.6% | 8 | C | |
| 7 | DE | 38.0% | 14.3% | 8 | C | |
| 8 | AC | 37.0% | 0.0% | 8 | C |
Top published rows on the benchmark's original scale.
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
MCP-Mark evaluates LLMs on their ability to use Model Context Protocol (MCP) tools effectively, testing tool discovery, selection, invocation, and result interpretation across diverse MCP server scenarios.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
Common questions about MCP-Mark.
Kimi K2.7 Code is currently ranked first with 81.1%.
MCP-Mark evaluates LLMs on their ability to use Model Context Protocol (MCP) tools effectively, testing tool discovery, selection, invocation, and result interpretation across diverse MCP server scenarios.
Yes. Higher values rank better for this benchmark.
8 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.