Evaluating AI brokers that use exterior instruments requires reasonable check eventualities that seize how practitioners compose instruments and iterate throughout dialog turns. Setting up such eventualities by hand calls for deep area experience, doesn’t scale throughout instrument ecosystems, and produces static benchmarks that can’t monitor evolving APIs. We observe that instrument specs—perform names, natural-language descriptions, and typed parameter schemas—already encode ample semantic info to synthesize reasonable analysis eventualities with out handbook curation or dwell instrument execution. Agent Seer builds off this latent info: from a single Mannequin Context Protocol (MCP) specification, with no examples, no dwell instrument entry, and no domain-specific tuning. This pipeline enriches uncooked schemas, generates graded eventualities with artificial instrument outputs, and expands them into mock-data-grounded multi-turn dialogues that exhibit robust tool-calling correctness and conversational coherence. Analysis high quality is measured by making use of this pipeline on seven MCP specs spanning various domains and tool-suite sizes and measuring the tool-calling correctness and conversational coherence. The pipeline achieves robust high quality throughout all domains, with full instrument protection on small and medium specs. Two findings emerge inside this evaluation: parameter schema complexity is the strongest correlate of high quality variation—tool-suite measurement performs a smaller, orthogonal position—and argument worth accuracy is the dominant failure mode amongst imperfect eventualities, a sub-dimension invisible to coarse-grained name-match metrics.
