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Agent Seer: Synthesizing Scenarios from Specification Understanding
Pipeline synthesizes evaluation scenarios from tool specifications
Apple researchers present Agent Seer, a pipeline that synthesizes realistic evaluation scenarios for tool-using AI agents from a single Model Context Protocol (MCP) specification without examples, live tool access, or domain-specific tuning.
The pipeline generates graded scenarios and multi-turn dialogues, achieving strong tool-calling correctness and conversational coherence across seven MCP specifications.
The analysis finds that parameter schema complexity is the strongest correlate of quality variation, with argument value accuracy as the dominant failure mode.
From the source
We observe that tool specifications—function names, natural-language descriptions, and typed parameter schemas—already encode sufficient semantic information to synthesize realistic evaluation scenarios without manual curation or live tool execution. Agent Seer builds off this latent information: from a single Model Context Protocol (MCP) specification, with no examples, no live tool access, and no domain-specific tuning.
machinelearning.apple.com
