Build a meeting prep and follow-up assistant with Amazon Quick and Cisco Webex MCP servers

· Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

An article details how to build a meeting preparation and follow-up assistant by integrating Amazon Quick with Cisco Webex Model Context Protocol (MCP) servers. This solution unifies disparate meeting-related tasks, allowing users to gather context from Webex meetings, Vidcast videos, and Webex message spaces through a single conversational workflow. The assistant leverages three Cisco Webex MCP servers—Webex Meetings MCP, Vidcast MCP, and Webex Messaging MCP—to find upcoming meetings, review prior summaries and transcripts, pull Vidcast highlights, and identify unresolved follow-ups. After a meeting, it can summarize discussions, identify action items, and draft follow-up messages. This integration aims to reduce time spent searching across collaboration tools, potentially saving 30 to 45 minutes per recurring meeting, by orchestrating information retrieval and action execution within Amazon Quick.

Key takeaway

For AI Engineers or Directors of AI/ML tasked with improving team productivity in collaborative environments, you should consider implementing conversational assistants like the Amazon Quick and Cisco Webex MCP integration. This approach centralizes meeting preparation and follow-up, reducing context switching and manual data retrieval. Start by enabling read-only MCP tools and validating context retrieval before gradually introducing controlled write actions to enhance workflow automation and ensure security.

Key insights

Integrating Amazon Quick with Cisco Webex MCP servers creates a unified conversational assistant for streamlined meeting preparation and follow-up.

Principles

Method

Configure Cisco Webex MCP servers (Meetings, Vidcast, Messaging) in Amazon Quick as Model Context Protocol connectors, enable specific tools, then create a custom chat agent with defined instructions for prep and follow-up workflows.

In practice

Topics

Best for: AI Engineer, Software Engineer, Director of AI/ML

Related on AIssential

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.