April 13
Introducing StoriesOnBoard MCP
We’re excited to introduce StoriesOnBoard MCP — our Model Context Protocol server that allows AI agents such as Claude Code, Cursor, and Claude Desktop to work with StoriesOnBoard through a standard MCP interface.
MCP (Model Context Protocol) is an open standard that lets AI agents connect to external tools and systems in a structured, reliable way. Instead of working only from the text in a chat, an MCP-enabled agent can discover data, read context, and perform actions through clearly defined tools.
In practice, this means AI agents can do more than just answer questions. They can work with real product data, understand the current state of a system, and help complete tasks inside the tools teams already use.
For StoriesOnBoard, MCP makes it possible for agents to explore story maps, read card details, discover valid reference data, and now also create and update cards. This opens the door to more practical workflows where AI can support backlog grooming, story map maintenance, and day-to-day product work in a much more direct way.
What StoriesOnBoard MCP can do?
With StoriesOnBoard MCP, agents can explore story maps and read card data using the existing tools:
ListStoryMaps
GetStoryMapCards
GetStoryMapCardDetails
In addition to the read access, MCP tools that allow AI agents to perform real write operations in StoriesOnBoard:
CreateStoryMapCard
UpdateStoryMapCard
GetStoryMapData
Together, these tools build on the existing MCP read capabilities and create a natural agent workflow:
ListStoryMaps → discover story maps
GetStoryMapData → discover collaborators, colors, statuses, annotations, priorities, and personas needed for valid write inputs
GetStoryMapCards → inspect the activity / task / subtask hierarchy and releases
CreateStoryMapCard → create a new card
UpdateStoryMapCard → update an existing card
GetStoryMapCardDetails → verify the result
This makes StoriesOnBoard MCP suitable not only for reading project context, but also for letting AI agents actively help manage and maintain story maps.
What can teams use it for?
In practice, StoriesOnBoard MCP can help AI agents support a range of product workflows across reading, creating, refining, and syncing work.
For example, teams can use it to turn a PRD into a first draft of a story map, identify missing stories in an existing flow, rewrite stories into a consistent format, suggest effort scores for unestimated work, generate release summaries from completed items, or sync a selected slice of work into Jira.
These are the kinds of workflows where MCP becomes especially useful: not just reading story map data, but helping teams actively shape, improve, and move work forward.
Example use cases
PRD to story map — An agent can read a PRD and turn it into a first draft of a story map with goals, steps, and initial user stories.
Gap analysis — An agent can inspect an existing onboarding flow or feature area, identify missing pieces, and suggest additional stories or acceptance criteria.
Story refinement — An agent can rewrite stories that do not follow the team’s preferred format and make the backlog more consistent.
Effort scoring — An agent can propose effort scores for stories that have not yet been estimated and highlight items that may need clarification first.
Release summary — An agent can review completed work in a release and turn it into a concise summary for stakeholders.
Sprint to Jira sync — An agent can take a selected release or sprint slice and create matching Jira issues to help move planned work into delivery.