Will Agent evaluation via MCP Stabilize Agent Networks?
summary
Claims that Model Contest Protocol (MCP) lets agents self-improve via evaluators that return numeric scores and text explanations; demonstrates a working Root Signals MCP server hooked into cursor and a simple Pydantic AI reservation agent. Shows a marketing-message optimization loop with 0–1 scores for persuasiveness and quality, and a concrete win: the reservation agent stops recommending the Acme Hotel after invoking a hotel share booking policy evaluator. Evaluators run repeatedly, can be selected or auto-picked, and require lifecycle maintenance and cost tradeoffs.
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