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claude — ~/projects/frontend
›look at my tickets in “To Do” and make a plan for how I should prioritize and finish them
janet-ai - list_tickets (MCP)(project_key: “SW”, status: “To Do”, assignees: [“me”])
janet-ai - list_tickets (MCP)(project_key: “SW”, status: “To Do”, assignees: [“me”])
You have 4 tickets in To Do. Here’s how I’d tackle them:
1. SW-84 SSO token refresh — blocking other auth work, fix first
2. SW-71 OAuth callback timeout — related to SSO, do right after
3. SW-63 Session cookie fix — standalone, quick win
4. SW-58 Safari redirect — lowest impact, last
Want me to spawn agents to start working through these?
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Give AI agents like Claude Code, Cursor, and Codex full access to your Janet AI workspace. Create & resolve tickets, manage sprints, and more.

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Setup

Run this command in your terminal:
claude mcp add janet-ai --transport http https://mcp.janet.ai/mcp
Then run /mcp in Claude Code and complete the OAuth flow to connect your Janet AI Organization.

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What You Can Do

Once connected, just talk to your AI agent naturally:
Use caseExample prompt
Find tickets”Show me my assigned tickets in MAIN”
Search”Search for tickets about authentication”
Create tickets”Create a high-priority bug for the login redirect issue”
Schedule tickets”Schedule a ticket for tomorrow at 9am to review the deployment”
Update tickets”Move MAIN-452 to In Progress”
Manage sprints”Add MAIN-100 and MAIN-101 to Sprint 3”
Create objectives”Create an objective called Q2 Launch with high priority”
Manage objectives”Add MAIN-100 and MAIN-101 to the Q2 Launch objective”
Add comments”Add a comment to MAIN-100 saying the fix is deployed”
Break down work”Create child tasks on MAIN-200 for each component”
Sort & organize”Sort the To Do column by priority”
Web search”What are the best libraries for rate limiting in Node.js?”
Write docs”Create a document called API Design with the proposed structure”

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Available Tools

The MCP server exposes 36 tools that your AI agent discovers and uses automatically:
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get_workspace_context
Get user info, organization, projects, statuses, and members.
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get_platform_guidance
Get platform documentation and usage guidance.
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list_tickets
List tickets with optional filters (status, priority, assignee, keyword, dates).
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get_tickets
Get full ticket details including description, comments, and child tasks.
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create_ticket
Create a ticket with title, description, status, priority, assignees, labels, sprint.
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update_tickets
Batch update ticket fields (status, priority, assignees, labels, due date).
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delete_ticket
Soft-delete a ticket. Restorable.
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restore_ticket
Restore a previously deleted ticket.
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move_ticket
Reposition a ticket: top, bottom, at:N, before/after, within kanban, sprint, or objective.
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search_tickets_semantic
Semantic search across tickets by meaning.
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list_child_tasks
List child tasks for a ticket.
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create_child_task
Create a child task on a ticket.
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update_child_task
Update a child task’s title, status, priority, or assignee.
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delete_child_task
Delete a child task.
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list_comments
List comments on a ticket.
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add_comment
Add a comment to a ticket.
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edit_comment
Edit an existing comment.
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delete_comment
Delete a comment.
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list_sprints
List sprints with status, dates, and ticket counts.
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create_sprint
Create a new sprint.
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update_sprint
Update a sprint’s name, dates, status, or description.
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add_sprint_tickets
Add tickets to a sprint.
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remove_sprint_tickets
Remove tickets from a sprint.
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list_sprint_tickets
List tickets in a sprint grouped by status.
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list_objectives
List objectives with status, priority, and ticket counts.
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create_objective
Create a new objective.
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update_objective
Update an objective’s name, status, priority, or assignees.
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delete_objective
Delete an objective.
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add_objective_tickets
Add tickets to an objective.
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remove_objective_tickets
Remove tickets from an objective.
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list_objective_tickets
List tickets in an objective grouped by status.
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list_documents
List documents in the workspace.
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get_document
Get a document’s full content.
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create_document
Create a document with title and markdown content.
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update_document
Update a document’s title or content.
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web_search
Search the web for current information.

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Activity Log

Every MCP tool call is logged and visible in the MCP page → Usage tab in your dashboard. Organization admins see all members’ calls; members see only their own.

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Troubleshooting

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Cannot connect / authentication errors

If your client fails to connect, try:
  1. Clear existing auth — In your client, remove and re-add the MCP server to trigger a fresh OAuth flow.
  2. Check your organization — During sign-in, make sure you select the correct organization.
  3. Restart your client — Some clients need a full restart after the OAuth flow completes.

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FAQ

The MCP server enforces per-user rate limits. If you hit a 429 Too Many Requests error, wait a minute before retrying. Typical limits are 200 requests/minute and 5,000 requests/hour per user.If you consistently hit rate limits, contact support@janet.ai.
mcporter lets you call MCP tools directly as CLI commands — no agent required.First, install it globally:
npm install mcporter
Authenticate once locally:
mcporter auth https://mcp.janet.ai/mcp
Then call any tool directly from the command line:
mcporter call janet-ai.list_tickets --project_key MAIN --status "To Do"
First, install it globally and authenticate on your local machine:
npm install mcporter
mcporter auth https://mcp.janet.ai/mcp
Credentials are stored in ~/.mcporter/credentials.json — copy that file’s contents as a secret or environment variable in your cloud environment so scripts can run without an interactive OAuth flow.For example, a Python script that creates a ticket:
import subprocess

subprocess.run([
    "mcporter", "call", "janet-ai.create_ticket",
    "--title", "Nightly health check failed",
    "--project_key", "MAIN",
    "--priority", "High"
])
Some agent runtimes support HTTP-based MCP servers but don’t implement the OAuth PKCE flow required by Janet AI (e.g. OpenClaw). You can proxy through mcp-remote to handle auth transparently.First, install it:
npm install mcp-remote
Then trigger the OAuth flow in your terminal:
mcp-remote https://mcp.janet.ai/mcp
Then configure your agent to use it:
{
  "mcpServers": {
    "janet-ai": {
      "command": "mcp-remote",
      "args": ["https://mcp.janet.ai/mcp"]
    }
  }
}
mcp-remote runs locally, performs the PKCE handshake with https://mcp.janet.ai/mcp on your behalf, and exposes the server over stdio to your agent. This works with any client that supports stdio-based MCP servers.