RecToolBench: Benchmarking Recommendation-Specific Tool Orchestration under Fuzzy User Intent
Year: 2026
Type: conference-paper
Abstract: Recent advances in agentic recommender systems are shifting recommender systems from passive filtering engines to instructionfollowing agents that use external tools to resolve user intent.However, existing benchmarks often assume explicit user intent, simplified tool environments, or isolated function calls, leaving realistic tool orchestration for recommendation underexplored.To bridge this gap, we propose RecToolBench, a Model Context Protocol (MCP)-based benchmark for evaluating tool-using recommender agents under fuzzy user instructions.Rec-ToolBench contains more than 1,200 executable tasks across three recommendation domains, 13 MCP servers, and 32 tools, spanning single-tool calls, parallel tool calls, sequential tool chains, and hybrid tool orchestration.We construct RecToolBench with a scalable synthesize-fuzzify-judge pipeline that generates executable fuzzy recommendation tasks, and evaluates agent trajectories using rule-based execution checks and rubric-based LLM evaluation.Experiments on representative LLMs show that syntactically valid tool calls do not guarantee successful recommendations.Models struggle with semantic parameter grounding, multi-step evidence integration, and grounded final recommendations, especially as orchestration complexity increases.Our results identify tool orchestration under fuzzy user intent as a major bottleneck for agentic recommender systems.Our data and code are available at https://github.com/ ShawnChenn/RecToolBench.
Institutions: Hong Kong Polytechnic University, Jilin University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7214574604