Agent Seer: Schema-Driven Synthesis of Tool-Agent Evaluation Scenarios
Agent Seer generates graded, multi-turn evaluation scenarios for tool-using agents from a single MCP specification, without manual curation, live tool execution, or domain-specific tuning. Across seven MCP specifications it reports complete tool coverage on small and medium suites and identifies parameter-schema complexity, not suite size, as the main correlate of quality; argument-value errors dominate imperfect scenarios but are invisible to name-match metrics.
Agent Seer presents a pipeline that synthesizes realistic multi-turn evaluation scenarios for tool-using agents directly from a static Model Context Protocol (MCP) specification, with no hand-written examples, no live tool access, and no domain-specific fine-tuning. The novel contribution is not an agent but a data generator that treats function names, descriptions, and typed parameter schemas as sufficient semantic content for benchmark construction. The constraint that moves is the dependence of evaluation-data creation on domain expertise and executable endpoints.1
Context
Conventional tool-agent evaluation relies on manually constructed scenarios or live tool execution, both of which scale poorly and become stale as APIs evolve. Agent Seer instead enriches the raw schema, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded multi-turn dialogues. In an evaluation across seven MCP specifications, it reports complete tool coverage for small and medium suites and strong dialogue quality, identifying parameter-schema complexity as the strongest correlate of quality variation.1
Assessment
The available evidence is a single self-reported evaluation with no comparison to existing benchmark generators or external validation set. The claim that argument-value accuracy is the dominant failure mode is the most important result because it names an error dimension that coarse name-match metrics cannot see; however, the source does not specify how this dimension is scored. Because the sample is only seven specifications, the reported correlational findings should be treated as hypotheses rather than established laws.1
Caveats
AI hypothesisConfidence in these results would increase with independent replication, a released artifact, and tests on large or rapidly changing specifications. The central 'sufficient semantic information' premise would be falsified if live tool execution proved necessary for valid grading, or if schema-only synthesis degraded markedly when parameter schemas become deep or heterogeneous. Until then, the headline numbers indicate feasibility, not equivalence with execution-based evaluation.1
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 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. This pipeline enriches raw schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded multi-turn dialogues that exhibit strong tool-calling correctness and conversational coherence. Evaluation quality is measured by applying this pipeline on seven MCP specifications spanning diverse domains and tool-suite sizes and measuring the tool-calling correctness and conversational coherence. The pipeline achieves strong quality across all domains, with complete tool coverage on small and medium specifications. Two findings emerge within this analysis: parameter schema complexity is the strongest correlate of quality variation—tool-suite size plays a smaller, orthogonal role—and argument value accuracy is the dominant failure mode among imperfect scenarios, a sub-dimension invisible to coarse-grained name-match metrics.
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