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Lets AI agents autonomously pull, work on, and update prioritized backlog tasks.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "sprinter" yet — see the docs or source repo.
Connect to the sprinter MCP server, read the current backlog, pull the top 3 unstarted tasks by priority score, generate an execution plan for each, and update their status to in progress.
A list of claimed tasks, an execution plan for each, and confirmation of successful status updates.
Use sprinter to find in-progress tasks assigned to me, select one that is finished, summarize the completion notes, key changes, and risk remarks, then update the task status to done.
A completion summary and an updated task record marked as done with attached notes.
From the sprinter backlog, filter high-priority tasks with satisfied dependencies, generate today's execution list in order, and label each task with estimated effort and blockers.
A prioritized task list for today with estimated effort, execution order, and potential blockers.
Create hierarchical task lists, break down work, and track priorities and progress.
Manage Azure DevOps work items, sprints, and boards using natural language.
Enable AI to read Backlog issues, comments, and attachments for project insight.
Schedule and run HTTP jobs with captured responses from your AI agent.
Turn multi-project workspace state into queryable context for AI agents.
Schedule recurring and one-time jobs with retries, priorities, and dependencies.