AI meeting notes and productivity assistants combine speech-to-text, summarization, and connected workflows to turn conversations into searchable records, action items, and context-aware knowledge. This topic covers evaluation of transcription quality (accuracy, speaker diarization, noise robustness, and latency), summarization style (abstractive vs extractive, action‑item extraction, and fidelity), and the integration surface needed to make notes usable across teams and systems. It is timely in late 2025 because real‑time agent participation and richer workspace connectors have moved from prototypes to production deployments, increasing demand for reliable transcription and safe, contextual access to company data. Key tools and connector categories include Voice Interaction Integrations (e.g., joinly’s MCP server to let AI agents join calls and interact via transcripts, voice, and chat), Document Management Integrations (Notion and Google Workspace MCP servers that expose Docs, Drive, Calendar and more), Knowledge Base Connectors (Obsidian MCP for vault search, reading, writing and organization), and Chat API Integrations (Slack MCP with multiple transport modes and OAuth/stealth options). MCP (Model Context Protocol) servers are central: they expose read/write/search tools and authentication flows so assistants can surface context, update notes, and push summaries or action items back into workflows. Practitioners should weigh transcription fidelity and speaker separation against latency and privacy controls; prefer MCP-enabled integrations when bidirectional, contextualized assistants are required; and consider compliance, multi-user OAuth, and on‑prem vs cloud hosting. The most useful systems blend accurate real‑time or post‑meeting transcription with robust summarization and tightly scoped connectors to documents, chat, and knowledge bases to keep meeting output actionable and discoverable.