We present Poly-FEVER, an open-source large-scale multilingual benchmark for hallucination detection through controlled factuality judgment in large language models. Poly-FEVER extends FEVER, Climate-FEVER, and SciFact to 77,973 Supported/Refuted claims aligned across 11 languages. We augment each claim with topic metadata from a 22-topic LDA model, enabling topic-aware analysis of multilingual factuality behavior. Translations are produced with Google Cloud Translation and validated by a targeted human audit, GEMBA, and COMETKiwi. We benchmark ChatGPT-3.5, LLaMA-2/3, Phi-3.5, and Mistral across three prompt families and evaluate robustness to model-generated rephrasing. We further analyze resource imbalance using web presence as a proxy for online claim availability, and evaluate retrieval-augmented factuality judgment with DPR over Wikipedia. Results show pronounced cross-lingual disparities under aligned claims, with stronger accuracy in high-resource languages such as English and Chinese and weaker accuracy in lower-resource languages such as Amharic and Tamil. Topic-aware prompting and retrieval provide selective gains, especially in Arabic and Amharic, while their effects vary across languages and can conflict with model priors. Poly-FEVER establishes a rigorous resource for responsible, language-adaptive hallucination detection and language-sensitive factuality evaluation. The proposed Poly-FEVER is available at: https://huggingface.co/datasets/HanzhiZhang/Poly-FEVER.

The Model Context Protocol (MCP) aims to standardize the integration of Large Language Models (LLMs) with external tools, yet existing research primarily evaluates functional capabilities while treating the underlying protocol as an opaque black box. This oversight obscures critical inefficiencies in token flows and latency distributed across MCP’s decoupled Host-Client-Server architecture. In this paper, we introduce ProMCP, an end-to-end profiling and instrumentation framework that decomposes the MCP workflow into a six-stage communication pipeline, enabling granular attribution of computational costs. We evaluate widely varying deployment topologies—from air-gapped local models to commercial off-the-shelf (OTS) clients—across 20 servers and 169 tools from MCP-Bench and MCP-Universe. Our analysis reveals a distinct inversion in performance bottlenecks: topologies with customized clients devote 56–72% of total tokens and 60–67% of latency to planning and schema injection, whereas OTS clients concentrate over 85% of latency in final answer synthesis. Crucially, actual tool execution constitutes a negligible fraction of the total cost across all configurations. These findings establish a quantitative baseline for protocol overhead and demonstrate that future optimization must target schema orchestration and transport efficiency rather than tool execution speed. The code is available at: https://github.com/ResponsibleAILab/ProMCP.