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.

Architecture overview

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.

Architecture diagram: the Smartsheet MCP Server connecting Amazon Quick and Kiro clients to the Smartsheet API and data layer

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

Sprint management with Amazon Quick and Kiro

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.

Prerequisites

Smartsheet MCP Server delivers precisely structured context, including row relationships, column types, cell history, and comments. This produces token-efficient payload sizes rather than returning raw API payloads. This engineering decision compounds into reduced cost and improved latency at scale when running hundreds of AI-assisted queries across a project portfolio. To learn how Smartsheet built their remote MCP server on AWS, see How Smartsheet built a remote MCP server on AWS.

Before connecting the Smartsheet MCP Server to Amazon Quick and Kiro, verify that you have the following in place:

An active AWS account with appropriate AWS Identity and Access Management (IAM) permissions for the service you’re connecting (Amazon Quick, or Kiro)
A Smartsheet account on a Business, Enterprise, or AWM plan.
A Smartsheet Personal Access Token, or OAuth credentials if your client supports the OAuth flow.
For Amazon Quick: Author or higher permissions in your Amazon Quick account.
For Kiro: Kiro IDE installed from kiro.dev with Node.js available in your environment.

The Smartsheet MCP Server is available at three regional endpoints. It uses Streamable HTTP transport (MCP spec versions 2025-06-18 and 2025-03-26) and is authenticated with either OAuth 2.0 or a Personal Access Token.

Commercial US: https://mcp.smartsheet.com.
EU: https://mcp.smartsheet.eu.
AU: https://mcp.smartsheet.au.

Both options expose the same Smartsheet toolset. The right choice depends on where your work gets done:

Choose Amazon Quick if you need a fully managed, organization-wide solution your whole team can use: no app registration, only pre-built OAuth, and it scales across knowledge workers in your enterprise.

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.

Amazon Quick integration

Amazon Quick includes Smartsheet as a pre-built connector in its directory. That means reading project status, updating rows, creating discussions, and surfacing risks, all in the same interface your business users already work in.

Amazon Quick makes that integration available across the organization, from project managers and operations teams to business users who manage work in Smartsheet.

To add the Quick integration:

In the Amazon Quick console, navigate to Connectors and find Smartsheet in the directory.

Amazon Quick Connectors directory with the Smartsheet connector tile

Figure 2: The Smartsheet connector in the Amazon Quick Connectors directory

Select the Smartsheet connector tile.
Enter the following integration details:
1. Name – Descriptive name for your Smartsheet integration.
2. Keep the Connection type and Auth configuration settings as is.
3. Base URL – https://mcp.smartsheet.com.
4. Client ID – App client ID from your Smartsheet Developer Tools app registration.
5. Client Secret – App secret from your Smartsheet Developer Tools app registration.
6. Token URL – https://api.smartsheet.com/2.0/token.
7. Auth URL – https://app.smartsheet.com/b/authorize.

Smartsheet connection details form in Amazon Quick with base URL, client ID, client secret, token URL, and auth URL fields

Figure 3: Entering the Smartsheet connection details in Amazon Quick

Choose Next.
On the Manage Tools & Permissions page, you see a list of available tools. Choose Next.

Manage Tools and Permissions page listing the available Smartsheet tools in Amazon Quick

Figure 4: The Manage Tools and Permissions page for the Smartsheet connector

On the Publish page, choose who can access this connection, and then choose Publish.

Amazon Quick Publish page for choosing who can access the Smartsheet connection

Figure 5: Choosing who can access the Smartsheet connection on the Publish page

Choose Next.

After you’re connected, your Amazon Quick chat agent can work with Smartsheet data through natural language:

“What projects in our portfolio are currently At Risk?”
“Update the status of the authentication task in the Backend API sheet to Complete.”
“Create a summary of all overdue items across the Q3 Roadmap and send it to the team.”

Amazon Quick chat response summarizing overdue tasks from a Smartsheet project sheet with recommended actions

Figure 6: Amazon Quick chat response summarizing overdue tasks from a Smartsheet project sheet with key observations and recommended actions

Kiro integration

Kiro is the right path when developers want Smartsheet project context available directly in their development environment, alongside their code, without context-switching.

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.

A Kiro feature called powers is a one-click installable bundle that packages a curated MCP server with steering files and hooks. It gives the Kiro coding agent a new capability out of the box. For Smartsheet, configuring the connection takes a single JSON configuration block and from that point, the Kiro agent can reference live project data as part of a development workflow.

To connect the Smartsheet MCP Server to Kiro, add it to your Kiro MCP configuration (.kiro/settings/mcp.json for workspace scope). For a full walkthrough, see Getting Started with Kiro and MCP Servers:

{
  "mcpServers": {
    "smartsheet-us": {
      "type": "http",
      "url": "https://mcp.smartsheet.com",
      "headers": {
        "Authorization": "Bearer <SMARTSHEET_API_TOKEN>"
      }
    }
  }
}

This configuration uses the Personal Access Token (API key) approach. For production deployments, OAuth is recommended.

Set SMARTSHEET_API_TOKEN to your Smartsheet Personal Access Token in your environment. Kiro loads MCP servers as background processes on startup. After it’s initialized, Smartsheet tools are available to the Kiro agent across all modes: chat, spec planning, and hooks-based automation. Full details in the Smartsheet MCP Server Documentation.

From there, Kiro can reference Smartsheet data as part of development workflows:

“Check the Backend API project sheet for any open bugs and generate a prioritized fix list.”

Kiro generating a prioritized fix list from open bugs in the Backend API Smartsheet project sheet

Figure 7: Kiro generating a prioritized fix list from open bugs in the Backend API project sheet

“Pull the Q3 Roadmap sheet and create a spec for the features marked In Progress.”

Kiro creating a spec from the in-progress features in the Q3 Roadmap Smartsheet sheet

Figure 8: Kiro creating a spec from the in-progress features in the Q3 Roadmap sheet

Enterprise governance

For builders deploying in regulated or enterprise environments, the Smartsheet MCP Server security model is built on the same infrastructure and permissions layer as the Smartsheet REST API. It’s not a new trust boundary:

OAuth 2.0 and Personal Access Token authentication: choose the auth method supported by your MCP client.
User-level permission enforcement: agents can only access data the authenticated user has permission to see. No privilege escalation through natural language prompts.
Organization-level AI controls: system administrators can turn on or off AI features globally. Write-tool access is configurable by user group.
Streamable HTTP transport: firewall allowlisting is done by fully qualified domain name (FQDN) (mcp.smartsheet.com), not IP address.

For compliance architects, the MCP Server doesn’t store prompts, and prompt content stays between the AI client and the model. Smartsheet sees only the structured tool calls and their results.

Clean up

If you followed this walkthrough and want to remove the integration afterward, here’s how to undo each path:

Amazon Quick

In the Amazon Quick console, navigate to Connectors.
Select your Smartsheet connector.
Choose Delete to remove the connection. This revokes the OAuth token and stops agent access to Smartsheet data.

Kiro

Open your MCP configuration file (.kiro/settings/mcp.json).
Remove the smartsheet-us entry from the mcpServers object.
Restart Kiro to unload the server.

Next steps

For background on how the server works, see How Smartsheet built a remote MCP server on AWS. After you’ve connected the Smartsheet MCP Server to your system of choice, here are four actions we recommend trying:

You can ask the chat agent to monitor your active project sheets, identify rows marked Blocked or At Risk. You can also ask to annotate projects with a discussion comment, and deliver a structured summary to your team through your preferred channel. This would be an example workflow that chains get_sheet_summary, search, and create_discussion_on_row.

The agent can also retrieve data through the Smartsheet MCP Server to reconcile task statuses between your issue tracker and your Smartsheet project plan. It can flag drift between the two, and update the affected rows, using get_sheet_summary, get_columns, update_rows, and create_discussion_on_row in sequence.

You can also try to aggregate project health signals across your reports and dashboards. You can use it to produce a structured briefing without manual data gathering, drawing on list_reports, get_report, list_dashboards, get_dashboard, and search.

Lastly, you can use the agent to follow every write with a discussion comment explaining what changed and why. This creates a human-readable audit trail inside Smartsheet through the combination of update_rows and create_discussion_on_row.

All four patterns work across the preceding paths: Amazon Quick for business-user-facing workflows, and Kiro for developer-initiated actions.

Building something with Smartsheet MCP and AWS? Share what you’re working on in the comments, or connect with us in the Smartsheet Developer Community.

About the authors

Galina Jordanowa

Galina is a senior product marketing manager II at Smartsheet. She translates complex AI and product capabilities into clear, strategic market narratives and go-to-market (GTM) motions. She brings 26+ years in B2B SaaS marketing and a dev background that means she actually gets how builders think. Throughout her career, she’s connected technical depth with business impact and stays genuinely energized by what’s next in AI and the new ways it’s reshaping how teams build and work.

Pyone Thant Win

Pyone is a Partner Solutions Architect at AWS, where she supports strategic ISV partners to bring AI-powered solutions to market. She works at the intersection of cloud infrastructure and partner networks, helping partners use AWS services from AI/ML capabilities to data integration patterns to build deeper, more intelligent product experiences. Her work bridges hands-on technical partnership and go-to-market strategy, delivering joint solutions and industry offerings that reach shared customers.

Vishnu Elangovan

Vishnu is a Worldwide Agentic AI Solution Architect with over a decade of experience in Applied AI/ML and Deep Learning. He loves building and tinkering with scalable AI/ML solutions and considers himself a lifelong learner. Vishnu is a trusted thought leader in the AI/ML community, regularly speaking at leading AI conferences and sharing his expertise on Agentic AI at top-tier events.