This topic covers how enterprise generative AI is being integrated with AI‑RAN (AI‑driven RAN control) and 5G network platforms through standardized toolchains, cloud/edge integrations, and on‑device models. In 2025 the combination of wider Open RAN adoption, private 5G deployments, and growing edge compute capacity has made low‑latency, contextual AI use cases—automated fault remediation, adaptive radio optimization, subscriber‑level insights—operationally practical. A common challenge is safely connecting LLMs and agents to cloud services, telemetry, and databases while preserving performance, governance and security; the Model Context Protocol (MCP) has emerged as a practical standard for that glue layer. Key categories and representative tools include cloud platform integrations (Azure, AWS, Cloudflare MCP servers exposing cloud capabilities and edge runtimes), cloud data platforms (managed storage and analytics for high‑cardinality 5G telemetry), data pipeline orchestration (Dagster for reproducible ETL and feature pipelines), on‑device LLM inference (models and runtimes optimized for edge/UE deployments to reduce round‑trip latency), and tool integrations (GitHub MCP server for repo access, Arize Phoenix for ML observability, Semgrep for code security, and MCP Toolbox for Databases for secure DB access). Together these components let operators build automated workflows where agents ingest telemetry, run inference at the edge or cloud, and trigger orchestration or network policy changes under audit‑friendly controls. Practical considerations include latency/locality tradeoffs, model governance and versioning, end‑to‑end observability, and security guardrails. For telecom teams evaluating AI‑RAN and GenAI platforms, focusing on standardized context interfaces (MCP), pipeline reproducibility, and hardened tool integrations yields more predictable, auditable deployments.