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What are MCP Prompts?

MCP Prompts are conversation templates that prime an AI assistant with context and step-by-step instructions for common workflows. Unlike tools (which perform actions) or resources (which provide reference data), prompts return pre-built messages that set up a guided conversation. When an AI client invokes a prompt, it receives one or more ChatMessage objects that frame the user’s intent and include any pre-loaded data. The AI assistant then responds naturally — using tools to carry out the steps described in the prompt. Prompts are read-only — they don’t modify data directly. They guide the AI to use tools.

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Available prompts

NameArgumentsDescription
setup_board—Guide through creating a new delivery board with containers, resources, and budget
plan_sprintboardId?Plan a sprint: select items, assign resources, and set up a cycle
review_sprintboardId?, cycleId?Review sprint progress: item status, completion, budget vs actuals
close_sprintboardId?, cycleId?Close a sprint: handle incomplete items, review outcomes, close the cycle
standupboardId?, cycleId?Daily standup: what’s done, in progress, and blocked in the current sprint

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Static vs dynamic prompts

  • Static (setup_board) — Returns fixed instruction messages. The AI discovers live data by calling tools during the conversation.
  • Dynamic (plan_sprint, review_sprint, close_sprint, standup) — Fetches live tenant data (boards, cycles, items) and includes it directly in the prompt messages, giving the AI immediate context to work with.
Dynamic prompts accept optional arguments to target a specific board or cycle. When omitted:
  • If the tenant has a single board, it’s auto-selected.
  • The most recent open cycle is auto-selected.
  • If multiple boards exist and no boardId is provided, the prompt lists all boards and asks the user to choose.

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How AI clients use prompts

Prompts are discovered and invoked via the standard MCP protocol methods: 
prompts/list   → returns all 5 prompts with names, descriptions, and argument schemas
prompts/get    → returns ChatMessage list for a specific prompt (with optional arguments)

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Example: Daily standup

An AI client calls prompts/get with: 
{
  "name": "standup",
  "arguments": {
    "boardId": "board-abc-123"
  }
}
The server responds with a ChatMessage containing:
  • The board and cycle context (name, dates, budget)
  • Items grouped by phase (Open, Working, Closed)
  • Instructions for the AI to present a concise standup summary
The AI assistant then formats this into a readable standup report and highlights blockers.

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Prompt details

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setup_board

Guides the user through creating a new board from scratch. The AI is instructed to:
  1. List available containers (get_containers)
  2. List available teams/groups (get_user_group_list)
  3. Ask about budget hours
  4. Create the board (create_board)
  5. Optionally create the first cycle (create_cycle)

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plan_sprint

Pre-loads boards, open cycles, and board containers. The AI guides the user to:
  1. Select or create a cycle
  2. Find unplanned items via get_items or search_items
  3. Plan items into the sprint with plan_item
  4. Summarize the planned sprint

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review_sprint

Pre-loads cycle items grouped by status phase (Open / Working / Closed), budget vs actual hours, and at-risk items (no estimate or no resource). The AI:
  1. Summarizes progress as percentage complete
  2. Highlights at-risk items
  3. Flags items unlikely to finish by sprint end
  4. Suggests corrective actions

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close_sprint

Pre-loads incomplete items and identifies the next open cycle for rollover. The AI guides:
  1. Deciding what to do with each incomplete item (move, unplan, or close)
  2. Summarizing sprint outcomes
  3. Closing the cycle via update_cycle

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standup

Pre-loads cycle items by phase for a quick daily summary. The AI presents:
  1. Done — Recently completed items
  2. In Progress — Active items with assignees
  3. Blocked / Needs Attention — Items stuck or missing data