Agentic product management with the right product context.
With an our new MCP server connection, user story mappings become the shared context layer for AI-driven product work: Claude and other agents read and write directly to live story maps, turning user goals, journeys, and acceptance criteria into persistent, structured memory. Instead of re-explaining context every session, agents and teams work from the same source of truth, continuously, across every program.
What's new: Product discovery and coding context for AI agents, built in
We've released a Model Context Protocol (MCP) server for StoriesOnBoard. In practical terms, this means AI agents, including Claude and other MCP-compatible assistants, can now read from and write to a live story map directly: user personas, journey steps, acceptance criteria, dependencies, and priorities, all in one structured and queryable place.
Until now, most teams using AI in product development were feeding it isolated prompts with no persistent memory of user goals or context. The MCP server changes that. The story map becomes the shared context an AI agent works from continuously, not a document that has to be re-explained every session. This is a meaningful step in an ecosystem that is moving quickly: as of December 2025, there are over 10,000 active public MCP servers and roughly 97 million monthly SDK downloads.
Discovery > Planning > Delivery > Coding
How Agentic Coding Works with StoriesOnBoard (Claude Code, Cursor, Codex & more)
StoriesOnBoard connects directly to the AI agents and coding tools your team already uses, like Claude, Cursor, VS Code, or ChatGPT, so they don't just "chat" about your project. They interact with it as an active collaborator.
Traditionally, AI could only help by generating text you then had to copy and paste into your story map. Now, the AI has a hands-on connection to your workspace. (Under the hood, this runs on the Model Context Protocol, or MCP, the emerging standard for connecting AI agents to tools.)
The MCP server allows agents to perform both comprehensive read and atomic write operations across the entire story map structure, unlocking powerful automation workflows:
Read & Discovery:
Agents can discover story maps within a workspace, retrieve the full hierarchical structure of activities, tasks, subtasks, and releases, and fetch full details (description, priority, estimation, status, attachments etc. of any specific card.
Write & Structural Changes:
Agents can create new cards, update existing card fields, and perform structural changes like reordering, re-parenting tasks, and rescheduling subtasks across releases with a single MCP call.
What You Can Do with StoriesOnBoard & AI Agents
The StoriesOnBoard MCP server offers seven specialized tools, covering discovery, read, and write capabilities:
CAPABILITY | Description |
Get story map structure | See the full backbone at a glance: activities, tasks, subtasks, and releases |
Get card details | Pull the description, priority, estimation, status, attachments, and links on any card |
Add cards | Create activities, tasks, or subtasks in one step, with description, color, estimation, and assignee already filled in |
Update card data | Edit a card's title, description, status, priority, or estimation without opening it manually |
Arrange cards on the map | Reorder activities, move a task under a different one, or shift a subtask between releases |
Manage releases | Set up or update a release's name, goal, dates, and status |
Manage personas | Create or update user personas, including descriptions and custom fields |
Manage annotations | Add and manage icon-based card annotations |
Manage colors | Add and customize card color label |
Benefits of Using StoriesOnBoard with AI Agents
The StoriesOnBoard Model Context Protocol (MCP) server represents a paradigm shift in product management. Instead of the AI being a separate chatbot where you copy-paste text, the MCP server turns the AI into an active collaborator with a "hands-on" connection to your actual story map.
By using an MCP-compatible host (like Claude Desktop, Cursor, or IDEs), your AI agent can see, understand, and edit your product's DNA in real-time.
1. Elimination of Manual Data Entry (Agentic Mapping)
The most immediate benefit is the transition from static documents to visual maps without the "manual labor.”
The Benefit:
You can provide a Software Requirements Document (SRD) or meeting notes to the AI and say, "Build the backbone and draft the first 20 stories."
How it works:
The AI builds the hierarchy, activities, tasks, subtasks, automatically (Activities → Tasks → Subtasks) in seconds, ensuring your map stays in sync with your latest documentation.
2. Intelligent Backlog Grooming & Quality Control
Backlog grooming is often the "chore" of product management. An AI agent can now perform this autonomously.
The Benefit:
The AI can scan your map for "thin" cards (missing descriptions, story points, or priority) and proactively fill those gaps.
Contextual Awareness:
The AI reads existing team comments and attachments to synthesize accurate Acceptance Criteria (AC) that reflect real team discussions, not just generic AI guesses.
3. Strategic "Slice" Planning (MVP Development)
Deciding what goes into an MVP vs. a later release is a complex logic puzzle.
The Benefit:
You can ask the AI to perform "Value-Based Slicing."Example:
"Identify the 'Happy Path' for a first-time user and move those cards to the MVP release." The AI evaluates the entire map and reorganizes your releases strategically.
4. Real-Time Gap Analysis
Because the AI can "see" the entire horizontal journey, it can spot architectural holes that humans might miss in a flat list.
The Benefit:
You can prompt the AI to find missing edge cases.Example:
"Look at our checkout flow. Did we account for a scenario where the user's payment method expires mid-session?" If not, the AI can instantly create the necessary cards to bridge that gap.
5. Bridging the "Context Gap" for Developers
When developers use AI-powered IDEs (like Cursor or Windsurf) connected to StoriesOnBoard via MCP, the AI understands the "Why" behind the code.
The Benefit:
The AI doesn't just see the code; it sees the User Story and the Activity it belongs to.
The Result:
This leads to more accurate code generation and fewer "reworks" because the AI has the full business context of the feature it is helping to build.
Where this creates value
We've been documenting how teams are putting this to work. A few examples that we think is really powerful in practice:
Use case | What it does | Read |
Deploying AI with real product context | Connects Claude to a live story map instead of one-off prompts, so the agent understands user goals and journeys, not just tickets | |
PI planning for SAFe programs | Keeps the user journey visible from portfolio epic to team story, and helps surface cross-team dependencies before planning starts | |
Enterprise migrations | Classifies existing workflows as supported, workaround, gap, or sunset, and sequences migration waves by workflow readiness rather than by system component | |
Legacy systems mapping | Lets an AI agent read an existing codebase and draft a discovery map, turning tribal knowledge into documentation in hours instead of days | |
From PRD to story map | AI reads a PRD and drafts the activities, journey steps, and stories for your team to review and refine | |
AI-assisted gap analysis | Reviews an existing story map for missing steps, weak outcomes, and risky handoffs before they become costly rework | |
Rethinking MVP definition | Helps teams slice a genuinely minimal, coherent MVP around a complete user journey rather than a partial feature set | |
Meeting notes to backlog | Turns workshop transcripts and call notes into a structured, prioritized backlog with drafted acceptance criteria |
More About Powerful Real-World Scenarios
The following examples show how teams of different sizes use AI agents with StoriesOnBoard today. Each scenario is self-contained. Pick the ones that match your situation.
Any Team
Meeting Notes to Backlog |
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Who benefits Product manager or founder who runs frequent customer calls The situation After a discovery call or team sync, you have a page of raw notes and no time to turn them into structured stories before the next sprint. What the agent does → Reads the meeting notes you paste or upload → Identifies activities, user needs, and edge cases → Creates structured user stories with acceptance criteria → Adds them to the correct release in your story map | Result A full sprint-ready backlog built while you are still on the call debrief. What used to take 2-3 hours takes under five minutes.
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Rethinking the MVP |
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Who benefits Founder or product lead facing scope creep The situation Your MVP has grown beyond what you can build in one release. You need to cut scope without losing the vision. What the agent does → Reads every card in the current MVP release → Identifies which stories map to your core value proposition → Proposes a trimmed-down release with justification → Moves non-essential cards to a later release | Result A leaner, more focused MVP definition backed by product logic, not guesswork.
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Onboarding a New PM |
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Who benefits New product manager joining an existing team The situation A new PM needs to understand the product scope, roadmap, and backlog quickly without spending a week reading documents. What the agent does → Reads the entire story map structure and all card details → Produces a written summary of the product scope → Explains what each release is targeting and why → Answers questions about specific features by referencing the map | Result The new PM ramps up in hours instead of days, with context grounded in the actual backlog.
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Post-Mortem to Product Improvement |
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Who benefits Team lead after a difficult sprint or product incident The situation After a post-mortem, the team has identified root causes and process improvements but no clear way to track them as product work. What the agent does → Reads the post-mortem notes → Identifies which improvements are product-level changes → Creates corresponding stories or updates existing cards → Links improvements to the relevant user journey in the map | Result Lessons from incidents become trackable product improvements, not forgotten action items.
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Growing Teams
These scenarios suit teams that have established processes, multiple stakeholders, and a growing need to align product work across functions.
OKR Alignment |
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Who benefits Head of product or VP managing quarterly planning The situation You have company OKRs but it is hard to see which backlog items are contributing to which key result. What the agent does → Reads your current OKRs and the full story map → Tags each activity and user story with the OKR it supports → Flags stories with no clear OKR alignment for review → Creates a release milestone for each key result | Result Every item in the backlog is connected to a business outcome. Planning reviews become focused conversations, not spreadsheet audits.
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Voice of Customer |
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Who benefits Product manager or UX researcher managing interview programs The situation You have conducted user interviews and have transcripts, notes, or synthesis documents. Turning these into structured product insights is time-intensive. What the agent does → Reads multiple interview summaries or transcripts you share → Extracts recurring user needs and pain points → Maps findings to existing user personas in the story map → Creates or updates user stories based on validated needs | Result Research drives the backlog directly. No more gap between what users say and what gets built.
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Agency Client Handoff |
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Who benefits Digital agency delivering a product to a client The situation At the end of a project, your client needs to understand what was built, why, and how to continue the roadmap without you. What the agent does → Reads the full story map built during the project → Generates a written handoff document in plain language → Summarizes what is in each release and what is still backlog → Suggests next steps and open questions for the client team | Result A clean, professional handoff that the client team can actually use. The story map becomes the living product document, not a locked Figma file.
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Competitive Feature Mapping |
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Who benefits Product manager doing competitive research The situation You need to understand how your product compares to competitors and identify gaps that should become backlog items. What the agent does → Reads a competitive analysis document you share → Maps competitor features to your existing story map activities → Creates placeholder stories for capabilities you are missing → Annotates existing cards where your implementation is weaker | Result Competitive intelligence lands directly in the backlog. Gaps are visible, prioritized, and ready for sprint planning.
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Enterprise
These scenarios involve coordinated programs, large teams, complex systems, and regulated environments where structure and traceability are essential.
PI Planning for SAFe Programs |
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Who benefits Release Train Engineer or Product Manager in a SAFe organization The situation Program Increment planning requires aligning multiple teams to shared objectives across a quarter. Preparing the PI backlog is labour-intensive. What the agent does → Reads PI objectives and team capacity you share → Organizes features into a PI-structured release in the story map → Breaks features into team-assignable user stories with AC → Flags dependencies and risks as annotations on specific cards | Result The PI backlog is structured, consistent, and traceable before the planning event starts. Teams spend the PI event aligning, not formatting.
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Enterprise Migration Planning |
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Who benefits Program manager leading a platform or system migration The situation Migrating a legacy platform involves hundreds of moving parts. It is hard to maintain a shared view of scope and progress across teams. What the agent does → Reads the migration scope document you share → Builds a story map covering all migration workstreams → Structures each workstream as an activity with phased releases → Creates user stories for each migration step with validation AC | Result The migration has a single visual source of truth. Every team knows what has shipped, what is in progress, and what is blocked.
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Legacy System Mapping |
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Who benefits Architect or product manager untangling a legacy codebase The situation Nobody has a clear picture of what the legacy system actually does. Documentation is missing or outdated. You need to build a map of the current system before you can replace it. What the agent does → Reads available documentation, diagrams, or code summaries you provide → Constructs a story map of the current system capabilities → Flags areas with no documentation or unclear ownership → Creates a second map for the target state and links gaps | Result A shared model of the current system, built in hours. Teams can now plan the replacement without rediscovering the same gaps every sprint.
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Source Code Mapping |
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Who benefits Engineering lead or product manager inheriting an undocumented codebase The situation A codebase has been acquired or handed over without product documentation. You need to understand what it does from the outside in. What the agent does → Reads code structure summaries, README files, or API specs you share → Derives user-facing capabilities from the technical structure → Builds a story map of what the system enables from a user perspective → Flags areas where the code and the intended product description do not align | Result An engineering-derived story map that gives non-technical stakeholders a product-level view of what the codebase actually does.
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Regulatory Compliance Mapping |
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Who benefits Product manager or compliance officer in a regulated industry The situation Compliance requirements such as GDPR, HIPAA, or SOC 2 have specific technical and product implications. Mapping these requirements to the backlog is tedious and error-prone. What the agent does → Reads the compliance requirements you share → Maps each requirement to the product areas it affects → Creates compliance-specific user stories with traceable AC → Annotates existing stories where compliance coverage is incomplete | Result Compliance work is visible in the backlog and tied to specific requirements. Auditors and engineering teams work from the same map.
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Stakeholder Interview Synthesis |
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Who benefits Business analyst or product manager running discovery across departments The situation A large product initiative requires input from a dozen or more stakeholders across departments. Synthesizing those interviews into a coherent product vision is one of the hardest parts of the job. What the agent does → Reads interview notes or summaries from multiple stakeholders → Identifies common themes, tensions, and priority differences → Creates a story map that reflects the synthesized product vision → Notes conflicting requirements as annotations for further discussion | Result A unified product map emerges from many voices. Disagreements are visible and flagged, not hidden in email threads.
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A story map does not just help product teams. When a coding agent reads your story map before writing code, the result is fundamentally different.
Without the story map The agent has access to:
| With the story map The agent has access to:
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Here is how a typical coding session works with StoriesOnBoard connected:
1 | Connect the MCP | Add StoriesOnBoard to your coding agent's configuration file. The agent now has access to your workspace. |
2 | Ask it to read the story map | Before writing any code, instruct the agent to read the relevant activity and its stories. This takes seconds. |
3 | Identify the walking skeleton | The agent reads the map and proposes the thinnest end-to-end slice of functionality to build first. |
4 | Write code story by story | The agent works through user stories in order, validating each implementation against the acceptance criteria in the card. |
5 | Update the map as work progresses | The agent marks stories as done, adds implementation notes, or flags blockers directly on the card. |
6 | Review and release | The story map reflects actual build progress. Your team sees the same view of done as the code base. |
Works with existing tools
If your development team uses Jira, GitHub Issues, or Azure DevOps, StoriesOnBoard syncs with those tools in real time and bidirectionally. The story map and the issue tracker stay in sync automatically. Your coding agent and your project manager are looking at the same thing.
Frequently Asked Questions
Does the agent change my story map without me asking?
No. StoriesOnBoard never changes your story map by itself. A change happens only when your AI agent makes one, and most AI apps ask for your approval before they do. The agent can never do more than you can: if you are a Viewer on a story map, your agent can only read it.
Can the agent see all my story maps, or just the ones I give it access to?
The agent signs in as you and works in the one workspace you choose when you connect it. It sees the same story maps you see, with the same role you have on each of them. Story maps you can't open stay hidden from the agent too. You can tell it which story map to work on by name or by pasting its link.
Is my data safe when an AI agent reads my story map?
Yes. You connect by signing in to StoriesOnBoard, so you never share your password with the AI app. The agent is limited to one workspace and to what you are allowed to see and do there. You can disconnect it any time in your profile, under Connected MCP Applications, by clicking Revoke. Keep in mind that the content the agent reads is processed by your AI provider (for example Claude or ChatGPT) under their own terms.
Do I need a developer to set this up?
No. Add StoriesOnBoard as a connector in your AI app, sign in with your StoriesOnBoard account, pick your workspace and click to approve. If your AI app asks for an address, use https://api.storiesonboard.com/mcp. There is nothing to install or code. Your subscription plan needs to include AI agent access.
Can the agent work with our existing Jira or Azure DevOps backlog?
The agent works on your story map, not directly in Jira or Azure DevOps. It can see which cards are linked to an issue. When it renames a linked card or edits its description, the issue is updated too, if you have turned on syncing for those fields in your integration settings. It can't change the status of linked cards, and new cards it creates are not sent to your issue tracker automatically.
What happens if the agent creates stories I do not want to keep?
Just delete them in StoriesOnBoard like any other card. The agent can't delete cards, so it can't remove your existing work. Everything the agent does is shown in the story map's activity with the name of your AI app, so its changes are easy to find.
Can multiple team members connect their agents to the same story map?
Yes. Each team member connects their own agent with their own account. Each agent has that person's access, and its changes appear under that person's name. Everyone sees the changes on the story map right away, as when teammates edit together.
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