Building Durable MCP Tools with Temporal
Learn how to build durable MCP (Model Context Protocol) tools using Temporal Workflows for reliable AI integrations. This two-part series shows you how to expose Temporal Workflows as MCP tools - first for a simple request-response weather server, then for a long-running invoice processor with human-in-the-loop approval.
In Part 1, you'll build a weather forecast MCP server that Claude Desktop can use to fetch real-time weather data from the National Weather Service API. You'll implement the tool using Temporal Workflows, which handle the API calls, retries, and state management automatically.
In Part 2, you'll add durable human-in-the-loop capabilities to a long-running invoice processing MCP tool with Temporal Signals, Queries, and durable timers.
Introducing MCP and Temporal
Build a weather forecast MCP server that Claude Desktop can use to fetch real-time data from the National Weather Service API. Implement the tool with Temporal Workflows so API calls, retries, and state management are handled automatically.
Start part 1 Part 2 - ~90 minAdding Human-in-the-Loop to MCP Tools
Add durable human-in-the-loop capabilities to a long-running invoice processing MCP tool. Use Temporal Signals, Queries, and durable timers to pause for approval, read live state, and survive crashes mid-wait.
Start part 2Get notified when we launch new educational content
New courses, tutorials, and learning resources - straight to your inbox.