Work Management for AI Agents with Smartsheet MCP on Amazon Web Services

AI agents become significantly more useful when they can interact with the real work happening across an organization. Project management and operations involve complex, cross-functional tasks, including updating timelines, reassigning responsibilities, tracking dependencies, and identifying project risks. Integrating AI agents with work management platforms can help teams perform these tasks without constantly switching between tools.

Smartsheet, a platform for managing projects, portfolios, and business operations, has introduced its generally available Model Context Protocol (MCP) Server. The integration enables AI assistants to read, create, update, and manage Smartsheet data through a standardized interface.

With more than 40 tools for working with sheets, rows, columns, and automations, the Smartsheet MCP Server enables users to manage project information through natural-language requests. Combined with AWS services such as Amazon Quick and the Kiro integrated development environment (IDE), it creates new opportunities to integrate AI into business and software development workflows.

What Is the Smartsheet MCP Server?

The Smartsheet MCP Server connects AI clients to Smartsheet through the Model Context Protocol, a standardized approach for enabling AI applications to interact with external tools and data sources.

Through this integration, AI assistants can retrieve project information, update task statuses, create rows, modify project structures, and interact with other supported Smartsheet capabilities.

Instead of manually navigating multiple sheets or building separate reports, users can describe the information or action they need in natural language. The AI client invokes the appropriate tools through the MCP Server and presents the results in the user's working environment.

This approach can help organizations reduce repetitive administrative work, improve access to project information, and incorporate AI assistance into existing processes.

Architecture Overview

The Smartsheet MCP Server acts as an integration layer between AI clients and the Smartsheet API and data layer. In the architecture described here, Amazon Quick and Kiro connect to the MCP Server as clients, providing different ways to access project data and perform work management tasks.

• Amazon Quick: Supports business users, project managers, and operations teams who want to query project information, update records, and create summaries using natural language.
• Kiro: Enables developers to access live Smartsheet project context directly from their development environment, including task assignments, specifications, and dependencies.
• Smartsheet MCP Server: Exposes supported Smartsheet operations through a standardized protocol.
• Smartsheet API and data layer: Provides access to project information according to the permissions of the authenticated user.

This architecture allows business users and developers to interact with the same project data through tools suited to their workflows.

Sprint Management with Amazon Quick and Kiro

Consider a software engineering team that manages its sprint in Smartsheet. Tasks, owners, statuses, deadlines, and blockers are recorded in a project sheet. Amazon Quick and Kiro can help different team members work with this information without leaving their preferred environments.

Amazon Quick for Project Managers and Business Users

A project manager who wants to identify blocked sprint tasks can submit a natural-language question to Amazon Quick. The chat agent queries the Smartsheet MCP Server, retrieves the relevant rows, and summarizes the results, including task owners and due dates.

The information comes from Smartsheet data, without requiring the project manager to manually assemble a report or dashboard for the request.

Kiro for Software Developers

Developers can use Kiro to retrieve the current sprint sheet and identify open backend tasks assigned to them. The AI agent accesses Smartsheet through MCP and presents the relevant tasks within the development workflow.

When a fix is committed, a configured Kiro hook can automatically update the corresponding Smartsheet row to mark the task as Complete.

These two integration paths connect project coordination with development execution, helping teams keep project information aligned with ongoing work.

Prerequisites

Before connecting the Smartsheet MCP Server to Amazon Quick or Kiro, make sure the following requirements are met:

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

The Smartsheet MCP Server provides three regional endpoints and supports Streamable HTTP transport. The source documentation identifies support for MCP specification versions 2025-06-18 and 2025-03-26, with authentication through OAuth 2.0 or a Personal Access Token. Both Amazon Quick and Kiro expose the same Smartsheet toolset, but they address different needs. Amazon Quick is suited to organization-wide use by business teams, while Kiro offers a lightweight option for developers who want project context within their coding workflow.

Integrating Smartsheet with Amazon Quick

Amazon Quick includes Smartsheet as a pre-built connector in its connector directory. Once configured, users can read project status, update rows, create discussions, and identify potential risks through the same interface they use for other business tasks.

Step 1: Find the Smartsheet Connector

Open the Amazon Quick MCP integration documentation, navigate to Connectors in the Amazon Quick console, and locate Smartsheet in the directory.

Step 2: Configure the Connection

Select the Smartsheet connector and enter the integration details required by your Smartsheet application registration:

• Name: A descriptive name for the integration.
• Connection type and authentication configuration: Keep the default settings as specified in the connector setup instructions.
• Base URL: https://mcp.smartsheet.com
• Client ID: The application client ID from Smartsheet Developer Tools.
• Client Secret: The application secret from Smartsheet Developer Tools.
• Token URL: https://api.smartsheet.com/2.0/token
• Authorization URL: https://app.smartsheet.com/b/authorize

Step 3: Review Tools and Permissions

Proceed to the Manage Tools & Permissions page and review the available tools. Continue to the Publish page, select who can access the connection, and publish it.

Access should be granted according to organizational requirements and the responsibilities of the intended users.

Step 4: Use Natural Language to Manage Projects

After the connection is established, users can ask Amazon Quick questions and request actions such as:

• "Which projects in our portfolio are currently marked At Risk?"
• "Update the authentication task in the Backend API sheet to Complete."
• "Summarize all overdue items across the Q3 Roadmap and prepare the summary for the team."

Amazon Quick can retrieve relevant Smartsheet information and return a structured response. Depending on the request and enabled permissions, it can also perform supported updates to the underlying project data.

Integrating Smartsheet with Kiro

Kiro is an agentic development environment designed to help developers turn high-level requirements into structured specifications, code, documentation, and tests.

Model Context Protocol servers allow Kiro to access external documentation, databases, APIs, and enterprise systems. Connecting Smartsheet through MCP gives the coding agent access to current project information rather than relying exclusively on information available during model training.

Configure the Smartsheet MCP Server

To connect Smartsheet to Kiro, add the MCP Server to the Kiro MCP configuration file. For workspace-level configuration, use .kiro/settings/mcp.json.

The following example uses a Smartsheet Personal Access Token for authentication:

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

Replace the placeholder with your Smartsheet Personal Access Token and configure the credential handling according to your environment's security requirements. The example uses token-based authentication; the source recommends OAuth for production deployments.

Kiro loads MCP servers during startup. Once initialized, the Smartsheet tools become available to the Kiro agent in supported workflows, including chat, specification planning, and hook-based automation.

For a detailed setup walkthrough, see Getting Started with Kiro and MCP Servers and the Smartsheet MCP Server documentation.

Use Project Data Directly in Development Workflows

Once the integration is configured, developers can use natural-language prompts to incorporate Smartsheet information into their work. For example:

• "Check the Backend API project sheet for open bugs and generate a prioritized fix list."
• "Retrieve the Q3 Roadmap sheet and create a specification for the features marked In Progress."

Kiro can retrieve the relevant project data and use it as context for planning and development. This helps developers connect implementation work with project priorities, task ownership, and delivery status.

Enterprise Governance and Security

For organizations operating in regulated or enterprise environments, access control and governance are essential considerations when connecting AI agents to business systems.

According to the Smartsheet MCP Server documentation, its security model uses the same underlying infrastructure and permissions layer as the Smartsheet REST API. The MCP integration therefore follows the existing Smartsheet access-control model rather than introducing a separate permissions layer.

The documented controls include:

• OAuth 2.0 and Personal Access Token authentication: The authentication method depends on the capabilities of the selected MCP client and the organization's configuration.
• User-level permission enforcement: Agents can access only the data available to the authenticated user. Natural-language prompts do not grant additional privileges.
• Organization-level AI controls: Administrators can enable or disable AI features at the organization level. Access to write tools can be configured by user group.
• Streamable HTTP transport: Network firewall allowlisting uses the fully qualified domain name (FQDN), such as mcp.smartsheet.com, rather than a fixed IP address.

The source documentation also states that the MCP Server does not store prompts. Prompt content remains between the AI client and the model, while Smartsheet receives structured tool calls and their results.

Organizations should review the current product documentation, authentication configuration, access policies, and data-handling requirements before deploying the integration in production.

Removing the Integration

If the integration is no longer required, it can be removed from the relevant client.

Remove the Amazon Quick Connection

1. Open the Amazon Quick console and navigate to Connectors.
2. Select the configured Smartsheet connector.
3. Choose Delete to remove the connection.

According to the documented workflow, removing the connection revokes the associated OAuth token and stops agent access to Smartsheet through that connection.

Remove the Kiro Connection

1. Open the MCP configuration file, such as .kiro/settings/mcp.json.
2. Remove the smartsheet-us entry from the mcpServers object.
3. Restart Kiro to unload the server.

If credentials were stored or distributed separately, revoke or rotate them as appropriate under your organization's security procedures.

Practical Use Cases and Next Steps

After connecting the Smartsheet MCP Server to Amazon Quick or Kiro, organizations can explore several workflows that combine project data retrieval with structured actions.

Monitor Project Health and Identify Risks

An AI agent can review active project sheets, identify rows marked Blocked or At Risk, add discussion comments, and prepare a structured status summary for the team. This workflow can combine sheet summaries, search, and discussion creation.

Reconcile Task Status Across Systems

Teams can use the MCP Server to compare task statuses in an issue tracker with the corresponding records in a Smartsheet project plan. The agent can identify discrepancies, flag affected tasks, update rows where authorized, and document the changes through discussion comments.

Generate Portfolio-Level Briefings

AI agents can aggregate project health indicators from Smartsheet reports and dashboards to prepare structured portfolio briefings. This reduces the need to manually collect information from multiple sources before a review.

Create an Audit Trail for Changes

Organizations can configure workflows in which an update is followed by a discussion comment explaining what changed and why. Combining row updates with discussion creation provides a human-readable record within Smartsheet.

These patterns can support both business-user workflows in Amazon Quick and developer-led workflows in Kiro. The exact level of automation depends on the tools enabled, permissions granted, and workflow configuration.

Learn More About Smartsheet MCP on AWS

To understand the underlying architecture and implementation approach, explore the following resources:

• How Smartsheet Built a Remote MCP Server on AWS
• Smartsheet MCP Server overview
• Amazon Quick MCP integration documentation
• Kiro MCP documentation

Conclusion

Integrating the Smartsheet MCP Server with Amazon Quick and Kiro enables AI agents to work with live project information through natural language. Business users can retrieve project status and manage tasks, while developers can incorporate project specifications, dependencies, and task updates directly into their development workflows.

By connecting AI assistants to existing work management processes, organizations can reduce repetitive administrative tasks, improve visibility into project status, and create more consistent workflows. Effective implementation still requires appropriate permissions, authentication, governance, and oversight of automated changes.

As an official Amazon Web Services partner, Softprom helps organizations explore AWS capabilities and evaluate cloud-based solutions aligned with their business and technical requirements. Contact Softprom to discuss AWS technologies and approaches for integrating AI into your organization's workflows.