GitScrum Sprints MCP. Analyze sprint performance and manage the full backlog.
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GitScrum Sprints manages your entire agile development lifecycle. It lets you plan, track, and report on sprints, epics, and user stories using natural language.
You can monitor KPIs, view burndown charts, and track velocity trends across multiple projects, all without jumping between dashboards. This tool gives your AI agent real-time visibility into project health and delivery cadence.
What your AI agents can do
All sprints
Lists all sprints across every workspace connected to the account.
Create sprint
Creates a new sprint record with defined start and end dates.
Create user story
Adds a new user story to the project's backlog.
Retrieves current sprint progress, detailed metrics, and overall Key Performance Indicators (KPIs) for immediate status checks.
Creates and lists user stories and epics, allowing you to structure the project scope before sprint planning begins.
Creates new sprints or updates existing ones, setting the official start and end dates for a development cycle.
Fetches detailed historical data, including burndown and burnup charts, for any specified sprint.
Lists tasks within a project, optionally limiting the view to tasks belonging to a specific sprint and filtering by status.
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GitScrum Sprints MCP Server: 15 Tools for Agile Management
These tools let your agent manage the full scope of your project, from creating the initial user story to calculating final sprint KPIs and generating historical reports.
019d8441all sprints
Lists all sprints across every workspace connected to the account.
019d8441create sprint
Creates a new sprint record with defined start and end dates.
019d8441create user story
Adds a new user story to the project's backlog.
019d8441get sprint
Retrieves all details for a single, specified sprint.
019d8441get task
Gets detailed information about one specific task using its unique ID.
019d8441list epics
Lists all epics currently defined within a project.
019d8441list sprints
Lists all sprints contained within a specific project.
019d8441list tasks
Lists tasks in a project, allowing filtering by a specific sprint or by status (todo, in-progress, done).
019d8441list user stories
Lists all user stories defined in a project.
019d8441sprint kpis
Retrieves key performance indicators (KPIs) for a given sprint.
019d8441sprint metrics
Gets detailed metrics for a sprint, including velocity and throughput data.
019d8441sprint progress
Gets the current completion status and task count for an active sprint.
019d8441sprint reports
Generates detailed sprint reports, including burndown, burnup, and task distribution charts.
019d8441sprint stats
Calculates and retrieves general statistical data for a sprint.
019d8441update sprint
Modifies an existing sprint's details or configuration.
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What you can do with this MCP connector
This MCP Server lets your AI agent manage your whole agile development cycle. You can plan, track, and report on sprints, epics, and user stories using plain language. You won't have to jump between dashboards to check project health or see delivery cadence.
Track Sprint Status and Performance
Your agent pulls current sprint progress and task counts using sprint_progress. It pulls key performance indicators (KPIs) for a given sprint with sprint_kpis. You can get detailed metrics, including velocity and throughput data, via sprint_metrics. For a complete look at history, sprint_reports generates detailed sprint reports, including burndown, burnup, and task distribution charts.
You can also run general statistical checks for a sprint using sprint_stats.
Manage the Backlog and Stories
You can structure your project scope before planning using list_epics to see all epics and list_user_stories to see all user stories. You can add new stories using create_user_story and manage the overall backlog by calling list_tasks, which lets you filter tasks by a specific sprint or by status (todo, in-progress, done).
You can also get specific info on any task using get_task.
Plan and Adjust Sprints
Need to start a new development cycle? Use create_sprint to make a new sprint record and define its start and end dates. If things change, you can modify an existing sprint's details or configuration with update_sprint. You can check all sprints across every workspace with all_sprints or see just the sprints for one project using list_sprints.
You can pull all details for a single sprint using get_sprint.
How GitScrum Sprints MCP Works
- 1 First, subscribe to the GitScrum Sprints integration on the Vinkius Marketplace and enter your GitScrum API token and company slug.
- 2 Next, ask your AI agent to perform a specific action—for example, 'Show me the velocity for the last sprint' or 'Create a new sprint for payments.'
- 3 The agent executes the necessary tool calls, retrieves the data, and delivers the sprint analytics or agile insight directly to you.
The bottom line is that your agent handles all the data fetching and analysis without you having to open and navigate the GitScrum web dashboard.
Who Is GitScrum Sprints MCP For?
This is for the Scrum Master who needs to run a standup meeting and instantly check the team's velocity. It's for the Product Owner who manages hundreds of user stories and needs to ensure the backlog stays aligned with product goals. It's for the Engineering Manager who tracks team throughput across multiple projects and needs to see systemic bottlenecks before they hit the deadline.
Reviews sprint KPIs and progress during standups and retrospectives, using tools like sprint_kpis to guide the team discussion.
Manages the user story and epic lifecycle, using list_user_stories and list_epics to define and prioritize the next batch of work.
Tracks team velocity and throughput across different sprints using sprint_metrics and all_sprints to report to stakeholders.
What Changes When You Connect
- See real-time status using
sprint_progress. Instead of manually clicking through dashboards, your agent tells you exactly how many tasks are done, in progress, and waiting in the backlog right now. It's instant, specific data. - Understand team capacity with
sprint_metrics. You get velocity and throughput numbers that prove if the team is on track or if they're going to miss the deadline. No more guessing. - Structure the work with
list_epicsandlist_user_stories. You can build out the entire product scope—from the big concept (epic) down to the smallest task—before the first sprint even starts. - Get historical context with
sprint_reports. Running this tool pulls up burndown and burnup charts for any sprint. You immediately see if the team consistently over-delivers or if they always run into scope creep. - Handle portfolio oversight using
all_sprints. You list sprints across all workspaces, which means you can check the status of Project A and Project B in one single query. Massive time saver. - Keep data accurate with
update_sprint. When plans change, you modify the sprint dates or scope directly through your agent, ensuring all connected systems reflect the latest reality.
Real-World Use Cases
Needs a quick status update before a standup.
The Scrum Master needs to know if the team is hitting its goals. Instead of logging into the dashboard and running through the metrics, they just ask their agent, 'What's the progress of the current sprint?' The agent uses sprint_progress and reports the percentage complete, the task counts, and the remaining days. The meeting starts fast, focused, and accurate.
Needs to plan a new, complex feature release.
The Product Owner knows they need a new module, but they don't know the scope. They ask their agent to help build the backlog. The agent uses list_epics to see existing large features, then uses create_user_story repeatedly to break down the new module into small, actionable stories. This process structures the entire development scope instantly.
Needs to compare performance across two different teams.
The Engineering Manager needs to compare Team Alpha's velocity against Team Beta's. They ask the agent to compare sprint_metrics for both. The agent runs the necessary calculations and presents a side-by-side comparison of story points and throughput. This comparison is impossible to do manually in two different toolsets.
Needs to fix a sprint that went off track.
A project is behind schedule. The manager asks the agent to review the historical performance. The agent uses sprint_reports to generate a burndown chart for the last three sprints, allowing the manager to pinpoint exactly where the team stalled (e.g., was it during task assignment or during testing?). This pinpoints the failure point immediately.
The Tradeoffs
Manually checking status by dashboard.
The user logs into the GitScrum site, navigates to the project board, clicks the 'Metrics' tab, finds the current sprint, and then manually reads the velocity number. This takes 5-8 minutes and requires the right screen to be open.
→
Just ask your agent: 'What is the current sprint progress?' The agent runs sprint_progress and provides the status and KPIs instantly in text, bypassing all UI navigation.
Using a basic task list.
The user calls list_tasks but forgets to specify the sprint_slug filter. They get a massive list of tasks from the whole project, making it impossible to tell what's relevant to the current cycle.
→
Always use the list_tasks tool and specify the relevant sprint slug filter. This ensures the agent only pulls tasks relevant to the current sprint cycle, keeping the data focused.
Creating scope without planning.
The user uses create_user_story for a feature, but never creates a corresponding create_sprint to contain it. The story exists, but it has no official deadline or context in the system.
→
First, use create_sprint to establish the working window. Then, use list_epics to define the container, and finally, use create_user_story to add the specific requirement within the structured context.
When It Fits, When It Doesn't
Use this if you need to model the entire project development lifecycle—from raw ideas (Epics) through defined work (User Stories) to measurable progress (Sprints). This is for teams that need to know why they are delayed, not just that they are delayed.
Don't use this if you only need to check the status of a single, isolated task. For that, get_task is fast and specific. Don't use this if you just need to see a list of all tasks in a project without knowing the sprint. Use list_tasks with a filter. This toolset requires you to think in structured agile cycles.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GitScrum. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 15 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sprinting status shouldn't require navigating five different dashboards.
Today, checking the status of a sprint means opening the board, finding the 'Metrics' tab, then clicking on the 'Burndown' chart, and finally cross-referencing that data with the 'Velocity' numbers displayed elsewhere. You end up jumping between three or four tabs just to get a simple status update.
With the GitScrum Sprints MCP Server, you just ask your agent: 'What is the progress of the current sprint?' The agent executes `sprint_progress` and gives you the percentage complete, the task breakdown, and the velocity trend in a single, clean response. You get the insight, not the dashboard.
GitScrum Sprints MCP Server: Manage sprints, tasks & epics
The manual process of planning involves manually setting dates, then listing the backlog, and then manually linking those stories to a specific sprint ID. This process is slow and prone to human error when juggling multiple projects.
Now, your agent handles the full cycle. You can run `create_sprint` to set the dates, and then immediately use `list_epics` and `create_user_story` to build out the required scope. The entire structure is built in natural language, saving hours of setup time.
Common Questions About GitScrum Sprints MCP
How do I use `sprint_kpis` to check performance? +
You pass the required sprint ID or slug to the sprint_kpis tool. This tool immediately pulls key performance indicators (KPIs) like velocity and task count, giving you a quick assessment of the sprint's health.
Can I find tasks in a specific sprint using `list_tasks`? +
Yes. When calling list_tasks, you must include the sprint_slug filter. This limits the results to only those tasks that belong to the designated sprint, ignoring all other project tasks.
What is the difference between `sprint_metrics` and `sprint_stats`? +
sprint_metrics provides detailed, quantifiable data like velocity and throughput. sprint_stats gives general statistical data. Use sprint_metrics when you need specific numbers for performance review.
How do I list all sprints across multiple projects? +
Use the all_sprints tool. This tool pulls data from every workspace connected to your account, giving you a comprehensive view of your entire portfolio's sprint schedule.
How do I use `list_user_stories` to filter by epic? +
You pass the epic ID as a filter to list_user_stories. This returns all user stories associated with that specific epic, making grouping and planning easier.
What should I do if `get_task` returns an empty UUID? +
An empty UUID usually means the task doesn't exist or the ID is incorrect. Double-check the UUID against the project's task list or confirm the task status.
How can I use `create_sprint` if I need to change the dates? +
When calling create_sprint, provide the desired start and end dates directly in the API call. The tool handles the date range and generates the necessary sprint ID.
Can `sprint_reports` pull data for tasks that are already closed? +
Yes, sprint_reports includes filters for historical data, allowing you to pull metrics and charts for completed tasks and past sprints.
Can my AI agent show me the burndown chart data for the current sprint? +
Yes! Use sprint_reports with the resource set to 'burndown'. Your agent returns the ideal versus actual burn-down data points, so you can visualize or analyze sprint health instantly. You can also request 'burnup', 'performance', or 'member_distribution' reports.
Can I see what tasks are in a specific sprint? +
Absolutely. Use list_tasks with the sprint_slug filter to see only tasks belonging to that sprint. You can further filter by status (todo, in-progress, done) to focus on what matters. Then use get_task to drill into any specific task for full details.
Does this integration support sprint velocity and metrics tracking? +
Yes. Use sprint_metrics for detailed velocity, throughput, and efficiency data. Combined with sprint_kpis for high-level indicators and sprint_stats for task distribution analysis, you get a complete performance picture across any sprint.
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