Workflow context for AI agents with Smartsheet MCP on AWS

AI agents are most useful when they can act on the real work happening across your organization. Running projects and operations 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 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 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.

Figure 1: Smartsheet MCP Server architecture showing MCP client connections from Amazon Quick and Kiro through the server to the Smartsheet API layer

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.

Choose Kiro if you’re a developer who wants something lightweight: no infrastructure, only a config file and a personal access token, scoped to your own workflow.

Kiro is built around spec-driven development. It turns high-level prompts into structured specs, then into working code, docs, and tests. MCP servers are how Kiro gets real-world context by connecting to documentation, databases, APIs, and enterprise systems to ground its agent in live data rather than training-time knowledge.

Consider a software engineering team that tracks their sprint in Smartsheet. Tasks, owners, statuses, and blockers all live in a project sheet. Here’s what each path looks like for the same team:

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. The response is generated from live Smartsheet data without building a report or dashboard.

For developers who are fixing bugs or working through features, they can ask Kiro to pull the current sprint sheet and show them the open backend tasks assigned to them. Kiro queries Smartsheet through MCP and surfaces the list inline. When the fix is committed, a Kiro hook automatically updates the corresponding row status to Complete.