An analysis of 69 AI-generated MCP servers revealed 73 instances of drift between their declared manifests and actual behavior after approximately six months in production. The majority of these divergences, two-thirds, were related to network activity, often stemming from dependencies pulling in undeclared network capabilities rather than explicit additions by the AI. Notably, the audit tool itself was the most significant offender, accounting for 46 of the divergences, highlighting a gap where human review over six months failed to detect these issues. AI
IMPACT Highlights the challenge of maintaining manifest accuracy in AI-generated code, especially concerning undeclared network dependencies.
RANK_REASON Analysis of AI-generated code drift from manifests, not a primary release or research paper.
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