Real-world agentic workflows you can build with the Smartsheet MCP server today

The way teams interact with work management is changing, fast. 

For over 20 years, Smartsheet has been where enterprises plan, execute, and scale their most complex operations. But getting meaningful answers from your data still requires human effort: opening sheets, filtering rows, writing summaries, chasing updates.

Smartsheet AI changes that reality. With two decades of workflow data and 150М+ automations powering our intelligence engine, we're building AI that understands how enterprises actually operate — not by replacing your AI strategy, but by making it more effective. Ask questions in plain language, surface insights instantly, and take action without touching a single row manually. The Smartsheet MCP Server is one powerful example: it connects MCP-compliant AI assistants and agents directly to your Smartsheet data through an open architecture designed to work with the tools you already use — whether Claude, Gemini, Copilot, or custom agents.

And because Smartsheet engineered proprietary data optimization directly into its MCP Server, every AI interaction you build with your preferred tools is fast, efficient, and cost-effective. Intelligently compressed payloads mean lower token costs for every external AI call, so you can focus on what matters: putting these capabilities to work.

Here are some workflows you can build today using only Smartsheet MCP Server and your AI. The examples below are just the beginning — the range of what you can ask, automate, and act on is virtually unlimited.

Instant portfolio status briefing

The problem: Executives and project leads spend disproportionate time assembling status updates from across multiple projects. By the time the picture is assembled, it's already outdated.

The workflow: Ask your AI assistant to scan across your project sheets and generate an instant portfolio briefing. The AI uses the Smartsheet MCP Server to search across accessible sheets, retrieve summaries, apply filters, and synthesize a coherent status picture — in seconds.

What this looks like in practice:

• "Summarize the status of all active projects in my Marketing workspace"
• "Which projects are behind schedule this week?"
• "What changed across my project portfolio since Monday?"

The AI reads your sheets, identifies patterns, and delivers a plain-language briefing with the information that actually matters — without anyone manually pulling data from five different sheets.

The result: Status visibility on demand. No assembly required. Executives get the signal without the noise, and project leads stop spending Friday afternoons writing reports that are stale by Monday.

Bottleneck identification across your project portfolio

The problem: Blockers rarely announce themselves clearly. They hide in status columns, comment threads, and overdue tasks scattered across sheets — invisible until they've already caused a delay.

The workflow: An AI agent searches across your sheets, identifies patterns in blocker columns, overdue tasks, and stalled rows, and surfaces the root causes that are slowing the most work. It can do this across your entire portfolio in a single interaction.

What this looks like in practice:

• "What's our biggest bottleneck right now? Look at what's blocking the most items across my sheets."
• "Which team members have the most overdue tasks assigned to them?"
• "Find all rows marked 'Blocked' across my Q2 project sheets and summarize the reasons."

The AI searches across multiple sheets simultaneously, identifies patterns, and returns a synthesized root cause analysis — the kind of insight that would previously take a PMO analyst hours to produce.

The result: Blockers surface before they compound. Leadership gets actionable intelligence, not raw data.

Automated weekly status report — without manual assembly

The problem: Weekly status reports are necessary but expensive to produce. Someone has to pull data, check for updates, calculate completion rates, and write a narrative. This is high-effort, low-creativity work that consumes PMO capacity.

The workflow: Every reporting cycle, ask your AI assistant or agent 
to generate a structured status report directly from your Smartsheet data. It retrieves the relevant sheets, applies date filters to identify what changed since the last report, calculates completion rates, and writes a narrative summary — ready to share.

What this looks like in practice:

• "Generate a weekly status report for the Q2 Product Launch project. Include completion rate, what's on track, what's at risk, and the top 3 items requiring attention."
• "Compare this week's project status to last week and highlight what changed."
• "Summarize all tasks modified in the past 7 days across my Operations workspace."

The AI can also create a new sheet or add rows to a designated reporting sheet, so the output is saved directly in Smartsheet — not just in a chat window.

The result: A report that used to take 2–3 hours is ready in minutes. Consistent, structured, and generated from live data — not from memory or last week's export.

Proactive deadline monitoring and escalation

The problem: Tasks slip silently. By the time a status update is manually entered, the window for course correction has often closed. Teams find out about missed deadlines in status meetings — the worst possible time.

The workflow: Use your AI assistant or agent to monitor your project sheets for tasks approaching or past their due dates, and take action directly within Smartsheet. The AI can identify at-risk tasks, update their status, add comments flagging the issue, and create summary escalation rows — all without leaving the sheet.

What this looks like in practice:

• "Find all tasks in my Project Tracker that are past their due date and haven't been updated in the last 3 days. Add a comment to each row flagging it for review."
• "Create a new 'Escalation Summary' row at the top of my sheet listing all overdue items in the Engineering workstream."
• "Update the status of all tasks due this week that are still marked 'Not Started' to 'At Risk'."

The AI identifies the right rows using date filters, then updates rows, adds comments, and creates escalation records — all within the existing permission model.

The result: Nothing falls through the cracks silently. Issues surface and are documented while there's still time to act — without requiring anyone to manually audit the sheet.

AI-assisted sheet creation and project setup

The problem: Setting up a new project in Smartsheet — creating the right sheet structure, columns, and initial rows — is repetitive work that slows teams down at the moment they most need to move fast.

The workflow: Describe the project you're starting, and let your AI assistant or agent build the initial sheet structure for you. It can create a sheet from a template, add the right columns for your use case, and populate initial rows based on your project plan — all through natural language.

What this looks like in practice:

• "Create a new project tracking sheet in the Q3 Initiatives workspace with columns for Task, Owner, Due Date, Status, and Priority. Add the first five milestone rows based on this project plan."
• "Set up a resource tracking sheet for the Design team with columns for Name, Current Project, Allocation %, and Available From."
• "Create a sheet from our standard project template in the Client Delivery folder for the new Acme engagement."

The AI builds the sheet end-to-end and can immediately confirm the structure is correct — saving the back-and-forth of manual setup and review.

The result: New projects go from kickoff to structured tracking in minutes, not hours. Consistent setup means better data quality across the portfolio — and less time spent on administrative overhead.

Built for scale: AI productivity without unpredictable costs

AI usage fees are quickly becoming the new cloud spend problem. Every query your team runs costs tokens — and as AI adoption scales across an organization, those costs compound fast. For IT leaders and finance teams evaluating enterprise AI, this is often the question that stalls pilots from moving to production: "What happens to costs when 500 people start using this?"

The Smartsheet MCP Server is purpose-built to address this. Rather than sending raw, unfiltered sheet data to the AI model — the way copy-pasting sheet contents into a chat window does — the MCP Server delivers precisely structured context: only the rows, columns, and metadata the AI actually needs to answer the question. The result is dramatically lighter data payloads, which translates directly to lower per-query token costs and faster response times.

In practical terms: a query across your entire project portfolio through the MCP Server consumes significantly fewer tokens than the same query run by manually exporting data to an AI assistant or agent. That efficiency doesn't require any configuration on your side — it's built into the protocol layer, so every query your team runs benefits automatically.

For organizations scaling AI adoption across teams and functions, this matters. Predictable, optimized token usage means you can expand AI-assisted workflows without a proportional increase in AI costs — and make a credible business case for moving from pilot to production.

How to get started

The workflows above don't require any Smartsheet training beyond what you already have. If your organization uses an MCP-compliant AI assistant or agent or is evaluating one — the path to getting started is straightforward.

Step 1: Check with your IT team. The Smartsheet MCP Server is now generally available. Ask your IT or technical team to connect your organization's AI assistant or agent to Smartsheet via the MCP Server. For them, it's a standard setup — the full technical documentation is here. For you, it's a one-time conversation.

Step 2: Connect with your existing Smartsheet permissions. Once set up, the AI accesses only the sheets and workspaces you already have permission to see. No new licenses. No reconfiguration of your existing data.

Step 3: Start asking questions. Begin with the workflow that would save your team the most time today. The prompts in this post are real starting points — not templates that require customization before they work.

The workflows above are a starting point. The real power is that every answer leads to the next question — and now, you can ask all of them without waiting for a report to be built or an update to be chased.

A note on enterprise readiness

Every workflow described here operates within the existing permission model. The AI can only access sheets and data that the authenticated user is authorized to see. All actions are logged. All changes are traceable. Organizations get full visibility into how AI interacts with their work data — who's asking what, which tools are being invoked, and how the data is being used — giving IT and leadership the control and auditability that enterprise AI adoption demands.

Ready to explore what's possible?  MCP Server documentation | Getting started guide

About Smartsheet MCP Server - The Smartsheet MCP Server is now generally available. It enables any MCP-compliant AI to read, write, and act on Smartsheet data as part of larger agentic workflows — opening new possibilities for automation, insight, and operational efficiency across enterprise teams.