Evaluating AI brokers that use exterior instruments requires real looking 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 device ecosystems, and produces static benchmarks that can’t observe evolving APIs. We observe that device specs—perform names, natural-language descriptions, and typed parameter schemas—already encode ample semantic info to synthesize real looking analysis eventualities with out handbook curation or dwell device execution. Agent Seer builds off this latent info: from a single Mannequin Context Protocol (MCP) specification, with no examples, no dwell device entry, and no domain-specific tuning. This pipeline enriches uncooked schemas, generates graded eventualities with artificial device outputs, and expands them into mock-data-grounded multi-turn dialogues that exhibit sturdy tool-calling correctness and conversational coherence. Analysis high quality is measured by making use of this pipeline on seven MCP specs spanning numerous domains and tool-suite sizes and measuring the tool-calling correctness and conversational coherence. The pipeline achieves sturdy high quality throughout all domains, with full device 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 dimension 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.
