Streamline MCP: Connect AI to your supply chain planning data

Supply chain teams rely on operational data across demand, inventory, orders, and capacity. Reaching an answer can require users to move through project views, filters, reports, and tables before they can examine the issue itself.

Streamline MCP gives them a more direct path.

Through the Model Context Protocol, Streamline can connect with compatible AI applications so users can ask questions in natural language and draw on information from their own Streamline environment. The same connection can make Streamline data available to AI agents, automated processes, and custom workflows.

What is Streamline MCP and how does it work?

Streamline MCP connects a Streamline environment with an AI application, agent, or automation platform that supports the Model Context Protocol. Compatible systems include ChatGPT, Claude, Microsoft Copilot, Google Gemini, and other applications with MCP support.

Once connected, the AI system can access the data and functionality exposed through MCP within the permissions and configuration of the signed-in Streamline user.

For planners and supply chain leaders, that shortens the path between a business question and the evidence behind it. A user can ask about shortages, open orders, forecast performance, or inventory risk without first piecing the analysis together across several views.

The connected AI application interprets the request and uses the available Streamline MCP capabilities to retrieve the relevant information.

How Streamline MCP changes access to planning data

Streamline MCP lets users begin with the issue they need to examine.

A planner may want to understand why certain items face shortages, review open orders, or compare actual sales with the forecast. Through MCP, the connected AI system can surface the relevant Streamline data and explore the question from there.

Go from a business question to the underlying data

Current MCP capabilities cover demand, inventory, forecasts, planned orders, reports, bills of materials, and capacity. Users can examine shortages, review open orders, compare forecast and actual sales, assess planned orders, or pinpoint supply chain risks that need attention.

Users can shape the response around the scope of their request and move from a broad question to a more focused investigation.

Build the analysis through follow-up questions

Users can continue refining the analysis through follow-up questions.

A user might begin with inventory risk, narrow the scope to a particular item group or period, compare the results, and then ask for a concise view of the issues that warrant attention. Each follow-up draws on the same Streamline environment, so users can shift the scope or examine another angle without rebuilding the analysis from scratch.

What can you do with Streamline MCP today?

Investigate planning issues

Streamline MCP gives users a conversational way to examine the planning information behind an operational question.

For decision-makers, that can mean tracing a high-level concern back to the underlying records, comparing related information, and narrowing the scope as the investigation develops. They can move from the initial issue to the supporting data without manually assembling each step.

Bring external context into the analysis

A connected AI system can also draw on information that sits outside Streamline.

Users can provide additional context and ask the AI to consider it alongside Streamline data. This supports analyses where part of the relevant business context resides elsewhere.

The connected AI application handles that reasoning without changing Streamline's calculation or forecasting engine.

Use Streamline data in AI workflows and agents

Streamline MCP can also serve as part of custom AI agents and automated workflows on platforms that support MCP.

For example, you can build an agent that combines Streamline data with information or processes from other systems. Streamline can supply planning data while the AI system coordinates work across the available sources and capabilities.

Streamline MCP and future API updates

Streamline MCP already provides access to the full range of data and actions available through the Streamline API. As Streamline expands its API, MCP will be maintained in line with those changes, giving connected AI systems access to newly available data and actions.

Connect Streamline MCP to ChatGPT or Claude

For setup instructions, authentication steps, and connection details, see the documentation.