Trust evaluation remains a key challenge in decentralized systems, where conventional reputation mechanisms often reduce complex human feedback to simple scores. This study employs the Model Context Protocol (MCP)—a standardized coordination framework—to connect Large Language Model (LLM) reasoning with blockchain-based verification. Under MCP supervision, contextual retrieval, semantic inference, and on-chain validation are organized into a reproducible workflow that converts unstructured textual data into verifiable trust metrics. The evaluation was conducted retrospectively on historical datasets from freelance marketplaces, DAO governance, and energy-sharing communities using an Ethereum-compatible test environment. No live deployment was performed. Results from this post-hoc analysis indicate that MCP coordination improves alignment accuracy by 22–28% over LLM-only baselines and by about 30% over rule-based heuristics, while reducing hallucination rates by 18–22%. Latency remained below 2.4 seconds per transaction, suggesting operational feasibility for decentralized infrastructures.