Agents
Agent Seer: Synthesizing Scenarios from Specification Understanding
Agent Seer synthesizes graded multi-turn tool-use eval scenarios from MCP specs alone—no examples, live tools, or domain tuning.
Agent Seer: Synthesizing Scenarios from Specification Understanding
What the paper does
Agent Seer synthesizes realistic agent evaluation scenarios from a single Model Context Protocol (MCP) specification. It enriches schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock-data-grounded multi-turn dialogues—without examples, live tool access, or domain-specific tuning.
Key findings
- Tool specs (names, descriptions, typed schemas) encode enough semantics to build evals without hand curation.
- Evaluated across seven MCP specs spanning domains and suite sizes; strong tool-calling correctness and conversational coherence, with full coverage on small/medium specs.
- Parameter schema complexity correlates most with quality variation; argument-value accuracy is the dominant failure mode missed by name-match metrics.
Why it matters
Makes agent evals scalable as MCP tool catalogs grow—critical when static hand-written benchmarks go stale against evolving APIs.
Related
- agent-seer-synthesizing-scenarios-from-specification-underst
- environment-free-synthetic-data-api-calling-agents
- uncertainty-quantification-for-llm-function-calling
Source: https://machinelearning.apple.com/research/agent-seer-synthesizing-scenarios | 2026-08-28