The Model Context Protocol (MCP) enables large language models to query and synthesize sports data across distributed sources, yet it lacks built-in mechanisms for provenance, integrity, and athlete-controlled access. This study proposes a hybrid MCP-blockchain architecture for LLM4Sports , a domain-specific model fine-tuned on localized sports datasets. In this design, MCP orchestrates multi-database retrieval, while a blockchain layer immutably anchors query and data-bundle hashes. Smart contracts manage time-bound and granular consent through decentralized identifiers and verifiable credentials, and auditable logs ensure end-to-end traceability. We contribute: (i) a trust-enhanced reference architecture combining MCP and blockchain; (ii) reusable prompt and workflow templates that embed consent validation within natural-language tasks (e.g., "Summarize athlete X's performance with verified consent"); and (iii) prototype implementations for both team analytics and personalized coaching. Evaluations on synthetic but realistic workloads show high verification accuracy and minimal orchestration overhead, demonstrating the feasibility of real-time, consent-aware analytics. The proposed framework enhances interoperability, regulatory compliance (e.g., GDPR), and athlete autonomy, while bolstering practitioner trust. We conclude by discussing scalability, legacy integration, and privacy trade-offs, and by outlining next steps toward field pilots and multimodal extensions (e.g., video provenance) across professional and amateur sports ecosystems.
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