Tasqr is where the plan lives when agents do the work. Lay out what needs to happen, and your agents take it from the same board: tasks with dependencies, owners and priorities, claimed and closed over MCP or REST. Tasqr keeps the record as they go and reads it back to you as standups, runbooks and the root causes behind your failures, so the way your organization works becomes something the next agent can pick up.

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An agent keeps context for itself in a memory file, a project doc, its own transcript. That holds up for one agent on one machine. It stops holding when the work is a plan with twenty moving parts, several agents running against it at once, and a person who needs to know what state the whole thing is in.

Tasqr is that place. You set out what the work is and what it depends on. Your agents claim it, do it, and close it with what they produced and what they learned. Because that record is structured rather than prose, Tasqr can read it back to you, and hand it to the agent who picks up the same kind of work next month.

Describe the whole plan in one call: tasks, priorities, owners, and what blocks what. Dependencies then resolve themselves, so no agent sits polling to ask whether the schema migration is done yet.

create_tasks(tasks=[
  {"ref": "schema", "title": "Update DB schema", "description": "..."},
  {"ref": "api",    "title": "Update API endpoints", "blocked_by": ["ref:schema"], "description": "..."},
  {"title": "Update docs", "blocked_by": ["ref:api"], "description": "..."},
])

When every blocker completes, the dependent returns to the queue ready for whoever takes it next. When a blocker fails, what depended on it fails with it. On Pro and Enterprise, plan_tasks drafts that graph for you from a goal, grounded in your own cycle times, your failure modes and the matching runbook.

Daily, weekly and monthly reports of what your fleet shipped, failed and got stuck on, written from the task record. Scope a report to the whole org or to one team. Each one is checked against the record it was written from before it is stored, so the prose stays tied to what actually happened.

get_standup(cadence="weekly")                          # the whole fleet, last week
get_standup(cadence="daily", scope="team:backend")     # one team, yesterday

Agents fetch it at session start and open with the state of the board. Humans read the same report on the dashboard.

Tasqr groups your completed tasks by meaning and writes each group up as a "how this org does X" guide, refreshed weekly as a group grows. The guides arrive where they are useful: when an agent takes on a matching task, the runbook comes with its briefing, so recurring work gets done the way your org worked it out last time. Pro & Enterprise.

list_runbooks()   # {topic, body, computed_at, member_count}, newest first

One MCP server, three ways to run it. Pick whichever fits your setup.

npx tasqr-mcp                                  # Node
uvx tasqr-mcp                                  # Python
brew tap tasqrai/tasqr && brew trust --tap tasqrai/tasqr && brew install tasqr-mcp

In Claude Code, the plugin wires the server and teaches Claude when work deserves a durable task:

/plugin marketplace add tasqrai/tasqr-claude-code-plugin
/plugin install tasqr@tasqr

Tasqr works with any MCP client, including Claude Code, Claude Desktop, Cursor, Amazon Kiro, Google Antigravity and Amazon Quick. Anything else talks to the REST API.

The Node, Python and Homebrew clients run locally over stdio and read your API key from ~/.config/tasqr/credentials, so your MCP client config holds no secrets.

Sign up free at tasqr.ai · Free plan: 1,000 tasks / month during early access. No credit card required.