No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by eval-sys · MCP Server · ★ 457
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcpmark safe to install? View the security audit →
MCPMark: Stress-Testing Comprehensive MCP Use An evaluation suite for agentic models in real MCP tool environments (Notion / GitHub / Filesystem / Postgres / Playwright). MCPMark provides a reproducible, extensible benchmark for researchers and engineers: one-command tasks, isolated sandboxes, auto-resume for failures, unified metrics, and aggregated reports. 🚀 MCPMark Verified is now the default. The standard tasks in this repository are the Verified set — every environment version-pinned and every verifier stabilized. Results from earlier task versions are deprecated and not directly comparable, so please report new numbers as MCPMark Verified. On the Verified set, (xhigh) leads at
| Stars | 457 |
| Forks | 42 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.7190947547699/100 |
| Open Issues | 19 |
| Last Updated | 2026-06-12 |
| Created | 2025-07-01 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
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mcpmark is MCPMark is a comprehensive, stress-testing MCP benchmark designed to evaluate model and agent capabilities in real-world MCP use.. It is categorized as a MCP Server with 457 GitHub stars.
mcpmark is primarily written in Python. It covers topics such as agentic, benchmark, eval-sys.
You can find installation instructions and usage details in the mcpmark GitHub repository at github.com/eval-sys/mcpmark. The project has 457 stars and 42 forks, indicating an active community.
mcpmark is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to mcpmark on Agent Skills Hub include forgemax, toolhive, mcp-router. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: