Agent Seer: Synthesizing Scenarios from Specification Understanding
Agent Seer is a pipeline that synthesizes realistic evaluation scenarios for AI agents from Model Context Protocol (MCP) tool specifications alone, requiring no manual curation, examples, live tool access, or domain-specific tuning The approach enriches raw schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded multi-turn dialogues with strong tool-calling correctness and conversational coherence Evaluated across seven MCP specifications spannin
Analysis
TL;DR
- Agent Seer is a pipeline that synthesizes realistic evaluation scenarios for AI agents from Model Context Protocol (MCP) tool specifications alone, requiring no manual curation, examples, live tool access, or domain-specific tuning
- The approach enriches raw schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded multi-turn dialogues with strong tool-calling correctness and conversational coherence
- Evaluated across seven MCP specifications spanning diverse domains and tool-suite sizes, achieving complete tool coverage on small and medium specifications
- Parameter schema complexity was identified as the strongest correlate of quality variation, while tool-suite size played a smaller, orthogonal role
- Argument value accuracy emerged as the dominant failure mode in imperfect scenarios, a sub-dimension invisible to coarse-grained name-match evaluation metrics
Why It Matters
This work addresses a critical bottleneck in AI agent evaluation: the labor-intensive, non-scalable process of manually constructing realistic test scenarios for tool-using agents. By demonstrating that raw tool specifications alone contain sufficient semantic information to generate high-quality evaluation data, Agent Seer enables dynamic benchmarking that can track evolving APIs across tool ecosystems. This has direct implications for how practitioners design evaluation pipelines and for the broader effort to standardize agent benchmarking.
Technical Details
- Agent Seer operates on Model Context Protocol (MCP) specifications, extracting semantic information from function names, natural-language descriptions, and typed parameter schemas without requiring live tool execution or domain-specific tuning
- The pipeline consists of three stages: schema enrichment, graded scenario generation with synthetic tool outputs, and expansion into mock-data-grounded multi-turn dialogues that maintain tool-calling correctness and conversational coherence
- Evaluation was conducted on seven MCP specifications across diverse domains and varying tool-suite sizes, measuring both tool-calling correctness and conversational coherence as quality metrics
- Key empirical finding: parameter schema complexity is the strongest predictor of scenario quality variation, while tool-suite size is a weaker, orthogonal factor
- A novel insight into failure modes: argument value accuracy is the dominant source of imperfection in generated scenarios, representing a sub-dimension that coarse-grained name-match metrics fail to capture
Industry Insight
- Benchmark construction for AI agents can be largely automated by leveraging existing tool specifications, reducing the dependency on scarce domain expertise and enabling continuous evaluation as APIs evolve
- Evaluation frameworks should adopt finer-grained metrics beyond name-match accuracy, particularly argument value accuracy, to properly diagnose agent failures in tool-use scenarios
- The finding that schema complexity—not tool-suite size—drives quality variation suggests that investing in richer, more detailed tool specifications will yield disproportionately better evaluation scenarios, guiding both tool designers and benchmark creators toward higher-quality documentation practices
Disclaimer: The above content is generated by AI and is for reference only.