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The comparison introduces a verifiable track record as a distinct evaluation axis for MCP servers, distinguishing tools that return auditable accuracy records through the MCP interface from those that only supply raw data or indicator output.
Practitioners building AI agents that rely on persistent memory — especially in correctness-sensitive domains like health, finance, or long-term projects — now have a structured breakdown of where each system's quality guarantees begin and end.