We Scored 14,800+ MCP Servers on Behavioral Trust. Here's What We Found.
The Model Context Protocol (MCP) ecosystem faces significant trust issues, as traditional static analysis methods fail to capture real-world server behavior. A new tool, Dominion Observatory, evaluates over 14,800 MCP servers based on their operational performance rather than just their source code. This behavioral trust scoring reveals critical insights into server reliability, availability, and potential anomalies that static analysis cannot detect.
- ▪Dominion Observatory provides behavioral trust scores for over 14,800 MCP servers, significantly expanding coverage compared to traditional static analysis.
- ▪The scoring methodology focuses on real-time server behavior, including success rates, latency profiles, uptime patterns, and interaction history.
- ▪Behavioral scoring can identify issues like degradation over time and inconsistent reliability across tools, which static analysis may overlook.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3874491) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Dinesh Kumar Posted on May 20 We Scored 14,800+ MCP Servers on Behavioral Trust. Here's What We Found. #ai #security #mcp #agents The MCP ecosystem has a trust problem — and scanning source code won't fix it The Model Context Protocol ecosystem is growing fast. Thousands of MCP servers now offer tools that AI agents call autonomously — executing code, querying databases, moving money, managing infrastructure.
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