Work management context for AI agents with Smartsheet MCP on AWS

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AI agents are most useful when they can act on the real work happening across your organization. Work management involves complex, cross-functional tasks such as updating project timelines, reassigning tasks, and surfacing status. AI agents can support you with these without anyone switching tools. Smartsheet, the work management platform for managing projects, portfolios, and operations, has launched a generally available Model Context Protocol (MCP) Server.

With the Smartsheet MCP Server, AI assistants can read, write, and manage Smartsheet data through a standardized protocol. The protocol spans more than 40 tools for exploring, creating, and managing sheets, rows, columns, and automations. You can update task statuses, create rows, and modify project structure directly through natural language.

In this post, we walk through two ways you can connect it to your existing workflows. Amazon Quick is an agentic AI workspace built for business users, project managers, and operations teams. They can ask questions, update sheets, and build dashboards through natural language without writing code. Kiro is an agentic IDE where developers connect to Smartsheet through MCP to reference project specs, update task status, and track dependencies without leaving their coding workflow.

The following diagram shows how the Smartsheet MCP Server sits as the central integration layer connecting to AWS services. Amazon Quick and Kiro connect directly as MCP clients.

Amazon Quick serves knowledge workers, project managers, and operations teams who want to query, update, and automate Smartsheet through natural language. With Kiro, developers can reference project specs, update task status, and track dependencies without leaving their coding workflow.

The following architecture diagram illustrates these integration paths: the Smartsheet MCP Server in the center, with Amazon Quick and Kiro as MCP clients on the left, and Smartsheet’s API and data layer on the right.

Consider a software engineering team that tracks their sprint in Smartsheet. Tasks, owners, statuses, and blockers all live in a project sheet.

When a user such as a project manager wants to check which sprint tasks are blocked this week, they can ask Amazon Quick through natural language. The Amazon Quick chat agent queries the Smartsheet MCP Server, pulls the relevant rows, and surfaces a summary with owner names and due dates.