Agent Seer: Synthesizing Scenarios from Specification Understanding
Apple Machine Learning Research
Agent Seer was introduced to synthesize realistic multi-turn tool-use evaluation scenarios directly from a single MCP tool specification instead of hand-built benchmarks. It was evaluated on seven MCP specifications and achieved complete tool coverage on small and medium specifications. As a result, it produces graded scenarios with synthetic outputs and improves measurements of tool-calling correctness and conversational coherence, highlighting argument value accuracy as the main failure mode and parameter schema complexity as the strongest quality correlate.
Why it matters
Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversation turns. Constructing such scenarios by hand demands deep domain expertise, does not scale across tool ecosystems, and produces static benchmarks that cannot track evolving APIs. 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…