Tasqr for AI assistants

Tasks that outlive the session

Claude Code, Cursor, Claude Desktop, Amazon Quick, Google Antigravity, Amazon Kiro: every assistant forgets the plan when the conversation ends. Tasqr gives them a task list that persists across sessions, machines and assistants, reached over MCP from whichever one you use, so tomorrow's session picks up where today's stopped.

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Free plan: 1,000 tasks / month during early access. No credit card required.

Why an assistant needs a task board

Most assistants keep an in-session todo list. It is a good scratchpad, and it stops at the edge of the session: it knows nothing about the subagents, the second laptop, the other assistant you switch to, or the teammate who picks the work up tomorrow. The moment work is measured in sessions rather than minutes, the plan has to live somewhere the next session can read it.

Tasqr is that somewhere. A task carries its title, description, priority, tags, dependencies, whoever holds it, and whatever the last agent produced. Tomorrow's session, a different model, or a different assistant reads the same record and continues. Nobody re-explains the project to start it again.

It is not only for code. A product manager in Claude Desktop or Amazon Quick tracks the launch checklist, the vendor follow-ups and the customer interviews on the same board the engineering agents work from, and the standups cover all of it.

Connect your assistant

Tasqr is an MCP server, so any MCP-capable assistant connects the same way: install the small local client, add one mcpServers entry, and the tasqr tools appear. The entry is identical everywhere; only where it lives differs. Most assistants accept it at two levels, per project or for every project.

JSON · mcpServers entry{
  "mcpServers": {
    "tasqr": { "command": "uvx", "args": ["tasqr-mcp"] }
  }
}
AssistantPer projectEvery project
Claude Code.mcp.json in the project, or install the plugin~/.claude.json via claude mcp add --scope user
Cursor.cursor/mcp.json~/.cursor/mcp.json
Google Antigravity.agents/mcp_config.json~/.gemini/config/mcp_config.json
Amazon Kiro.kiro/settings/mcp.json~/.kiro/settings/mcp.json
Claude DesktopGlobal onlyclaude_desktop_config.json (Settings › Developer › Edit Config)
Amazon Quick DesktopGlobal onlySettings › Capabilities › MCP › Add MCP, as a local command or by importing a Kiro or Claude Code config file

The first time the client starts without a key it walks you through signup and stores the key in a credentials file, so the config holds no secrets. Full details, including Homebrew and Node installs, are in MCP setup.

Claude Code: the plugin

For Claude Code there is a plugin that bundles the MCP client with a skill and installs in two commands:

CLAUDE CODE · install/plugin marketplace add tasqrai/tasqr-claude-code-plugin
/plugin install tasqr@tasqr

The status line is a Claude Code extra too: it shows the task you are on, what the queue would hand you next, and what is blocked, and it is read-only. Setup is in the docs.

Teaching the assistant when to use it

An MCP server gives an assistant tools; it still needs judgement about when to reach for them. The plugin's skill supplies that for Claude Code, and it is plain markdown you can adapt into any other assistant's rules or instructions. What it teaches:

What Tasqr adds that a file cannot

Duplicate detection

Creating a task returns open tasks that already match it, so two sessions never grind on the same thing.

Semantic search

search_tasks recalls past work by meaning, including completed tasks' output, so the assistant reuses prior work.

A briefing with every task

Every task an assistant takes arrives with the parent chain, the outputs of its blockers, and prior art. No follow-up fetches to rebuild context.

Standups written for you

Daily, weekly and monthly reports of what your agents did, generated from the task record.

One board, several people

A Tasqr workspace is shared. Your sessions, a colleague's in a different assistant, and any other agent or script that holds a key all read and write the same tasks, with roles, a tag vocabulary and an immutable history of who changed what. See multi-agent coordination for how a fleet of agents shares one queue without colliding.