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.
Related readings and updates.
PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning
May 4, 2026research area Speech and Natural Language Processing, research area Tools, Platforms, Frameworks
Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, training such agents using outcome-only rewards suffers from credit-assignment ambiguity, obscuring which intermediate steps (or tool-use decisions) lead to success or failure. In this paper, we propose PORTool, an importance-aware policy-optimization algorithm that…
Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents
May 1, 2026research area Methods and Algorithms, research area Tools, Platforms, FrameworksWorkshop at ACL
This paper was accepted at the Fifth Workshop on Natural Language Generation, Evaluation, and Metrics at ACL 2026.
Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-hoc. Disconnected from the active execution loop, such assessments identify errors that are usually addressed through prompt-tuning or retraining, and fundamentally cannot…
