{"slug": "work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws", "title": "Work management context for AI agents with Smartsheet MCP on AWS", "summary": "Smartsheet has launched a generally available Model Context Protocol (MCP) Server that lets AI assistants read, write and manage Smartsheet data through a standardized protocol spanning more than 40 tools for sheets, rows, columns and automations. An AWS blog post details connecting the server to Amazon Quick, an agentic AI workspace for business users, and Kiro, an agentic IDE for developers, so teams can query blocked sprint tasks and update row statuses in natural language. Smartsheet says the server returns structured context such as row relationships, column types, cell history and comments in token-efficient payloads rather than raw API responses, reducing cost and latency at scale.", "body_md": "## [Artificial Intelligence](https://aws.amazon.com/blogs/machine-learning/)\n\n# Work management context for AI agents with Smartsheet MCP on AWS\n\nAI 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](https://www.smartsheet.com/), the work management platform for managing projects, portfolios, and operations, has launched a generally available Model Context Protocol (MCP) Server.\n\nWith [the Smartsheet MCP Server](https://developers.smartsheet.com/ai-mcp/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.\n\nIn this post, we walk through two ways you can connect it to your existing workflows. [Amazon Quick](https://aws.amazon.com/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](https://kiro.dev/) 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.\n\n## Architecture overview\n\nThe 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.\n\nAmazon 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.\n\nThe 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.\n\n## Sprint management with Amazon Quick and Kiro\n\nConsider 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:\n\nWhen 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.\n\nFor 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.\n\n## Prerequisites\n\nSmartsheet 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](https://aws.amazon.com/blogs/machine-learning/how-smartsheet-built-a-remote-mcp-server-on-aws/).\n\nBefore connecting the Smartsheet MCP Server to Amazon Quick and Kiro, verify that you have the following in place:\n\n- An active AWS account with appropriate AWS Identity and Access Management (IAM) permissions for the service you’re connecting (Amazon Quick, or Kiro)\n- A Smartsheet account on a Business, Enterprise, or AWM plan.\n- A Smartsheet Personal Access Token, or OAuth credentials if your client supports the OAuth flow.\n- For Amazon Quick: Author or higher permissions in your Amazon Quick account.\n- For Kiro: Kiro IDE installed from [kiro.dev](https://kiro.dev) with`Node.js` available in your environment.\n\nThe 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.\n\n- Commercial US: https://mcp.smartsheet.com.\n- EU: https://mcp.smartsheet.eu.\n- AU: https://mcp.smartsheet.au.\n\nBoth options expose the same Smartsheet toolset. The right choice depends on where your work gets done:\n\nChoose 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.\n\nChoose 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.\n\n## Amazon Quick integration\n\nAmazon 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.\n\nAmazon Quick makes that integration available across the organization, from project managers and operations teams to business users who manage work in Smartsheet.\n\nTo add the Quick integration:\n\n1. In the [Amazon Quick console](https://docs.aws.amazon.com/quicksuite/latest/userguide/mcp-integration.html) , navigate to**Connectors** and find Smartsheet in the directory.\n\n1. Select the **Smartsheet connector** tile.\n2. Enter the following integration details: \n         \n  1. **Name** – Descriptive name for your Smartsheet integration.\n  2. Keep the **Connection type** and**Auth configuration** settings as is.\n  3. **Base URL** –[https://mcp.smartsheet.com](https://mcp.smartsheet.com) .\n  4. **Client ID** – App client ID from your Smartsheet Developer Tools app registration.\n  5. **Client Secret** – App secret from your Smartsheet Developer Tools app registration.\n  6. **Token URL** –[https://api.smartsheet.com/2.0/token](https://api.smartsheet.com/2.0/token) .\n  7. **Auth URL** –[https://app.smartsheet.com/b/authorize](https://app.smartsheet.com/b/authorize) .\n\n1. Choose **Next** .\n2. On the **Manage Tools & Permissions** page, you see a list of available tools. Choose**Next** .\n\n1. On the **Publish** page, choose who can access this connection, and then choose**Publish** .\n\n1. Choose **Next** .\n\nAfter you’re connected, your Amazon Quick chat agent can work with Smartsheet data through natural language:\n\n- *“What projects in our portfolio are currently At Risk?”*\n- *“Update the status of the authentication task in the Backend API sheet to Complete.”*\n- *“Create a summary of all overdue items across the Q3 Roadmap and send it to the team.”*\n\n## Kiro integration\n\n[Kiro](https://kiro.dev/docs/mcp/) is the right path when developers want Smartsheet project context available directly in their development environment, alongside their code, without context-switching.\n\nKiro 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.\n\nA 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.\n\nTo 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](https://repost.aws/articles/ARuX8rkojgSx-TYCc65JyAOw/getting-started-with-kiro-and-mcp-servers-connect-your-ai-ide-to-real-world-tools):\n\nThis configuration uses the Personal Access Token (API key) approach. For production deployments, OAuth is recommended.\n\nSet `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](https://developers.smartsheet.com/api/smartsheet/ai-integration/mcp-server).\n\nFrom there, Kiro can reference Smartsheet data as part of development workflows:\n\n- *“Check the Backend API project sheet for any open bugs and generate a prioritized fix list.”*\n\n- *“Pull the Q3 Roadmap sheet and create a spec for the features marked In Progress.”*\n\n## Enterprise governance\n\nFor 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:\n\n- **OAuth 2.0 and Personal Access Token authentication** : choose the auth method supported by your MCP client.\n- **User-level permission enforcement** : agents can only access data the authenticated user has permission to see. No privilege escalation through natural language prompts.\n- **Organization-level AI controls** : system administrators can turn on or off AI features globally. Write-tool access is configurable by user group.\n- **Streamable HTTP transport** : firewall allowlisting is done by fully qualified domain name (FQDN) (mcp.smartsheet.com), not IP address.\n\nFor 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.\n\n## Clean up\n\nIf you followed this walkthrough and want to remove the integration afterward, here’s how to undo each path:\n\n### Amazon Quick\n\n1. In the Amazon Quick console, navigate to **Connectors** .\n2. Select your Smartsheet connector.\n3. Choose **Delete** to remove the connection. This revokes the OAuth token and stops agent access to Smartsheet data.\n\n### Kiro\n\n1. Open your MCP configuration file (`.kiro/settings/mcp.json` ).\n2. Remove the `smartsheet-us` entry from the`mcpServers` object.\n3. Restart Kiro to unload the server.\n\n## Next steps\n\nFor background on how the server works, see [How Smartsheet built a remote MCP server on AWS](https://aws.amazon.com/blogs/machine-learning/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:\n\nYou 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`.\n\nThe 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.\n\nYou 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`.\n\nLastly, 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`.\n\nAll four patterns work across the preceding paths: Amazon Quick for business-user-facing workflows, and Kiro for developer-initiated actions.\n\nBuilding something with Smartsheet MCP and AWS? Share what you’re working on in the comments, or connect with us in the [Smartsheet Developer Community](https://community.smartsheet.com/categories/api-developers).", "url": "https://wpnews.pro/news/work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws", "canonical_source": "https://aws.amazon.com/blogs/machine-learning/work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws/", "published_at": "2026-10-20 17:59:07+00:00", "updated_at": "2026-10-20 18:18:47.156930+00:00", "lang": "en", "topics": ["ai-agents", "agent-protocols", "ai-tools", "ai-products"], "entities": ["Smartsheet", "Amazon Web Services", "Amazon Quick", "Kiro", "Model Context Protocol"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws", "markdown": "https://wpnews.pro/news/work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws.md", "text": "https://wpnews.pro/news/work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws.txt", "jsonld": "https://wpnews.pro/news/work-management-context-for-ai-agents-with-smartsheet-mcp-on-aws.jsonld"}}