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Task tracking for AI agents: the options

There are several ways to give agents a task list. They split on three questions: is the list shared between agents and machines, does it coordinate them (dependencies, who may take what), and does it learn from what they did. Here is where Tasqr sits, as fairly as we can put it.

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This page describes other products from their public documentation as of August 2026. If we have something wrong, tell us and we will fix it.

At a glance

OptionWhat it isShared across agents and machinesCoordinates agentsLearns from the board
TasqrHosted task board with an MCP server and a REST API, built for agents as the workersYes: one workspace, any number of agents, people and runtimesDependencies that auto-unblock, a tag vocabulary, teams and roles, one queue every agent draws fromDuplicate detection, semantic search, routing, standups, failure clusters, runbooks
Your assistant's built-in listThe in-session todo list in Claude Code, Cursor and the restOne assistant, usually one machineNo dependency graph; nothing for a second agent to draw fromNo
Hosted MCP task managersMCP-native task managers for coding agents with a priority queue and a get-next-task toolYes, hostedPriority queue; dependencies vary by productNot that we have seen
taskqueue-mcp, agent-task-listOpen-source MCP servers that expose a task queue from a local process or SQLite file, some with human approval checkpointsOne machine, unless you host and share the file yourselfQueue and approval steps; dependencies vary by projectNo
Jira, LinearProject trackers built for human teams, reachable by agents through integrations and MCP connectorsYes, hostedWorkflows and assignments designed for peopleAI assist features aimed at humans
A markdown file in the repoTODO.md, a plan file, a checklistOnly through gitNoNo

Versus your assistant's built-in task list

Claude Code, Cursor and the other coding agents each keep a todo list for the session in front of you. It is the right tool for small work, and Tasqr's skill deliberately leaves it alone for that. The gap opens when the work is shared: a second machine, a different assistant, a teammate, a subagent that needs context the list does not hold, or a queue that several agents pull from. Tasqr adds the shared board, dependency resolution and the audit trail, and connecting it is one MCP entry in any of them.

Versus hosted MCP task managers

These are the closest neighbours: hosted, MCP-first, and built around an agent asking for its next task. Tasqr shares that shape. Where it differs is what surrounds the queue and what happens after the work is done. Dependencies are a graph the board resolves on its own; tags and teams decide which agent may take which task. And the board is read back to you: duplicate detection on create, semantic search over history, routing based on each agent's track record, generated standups, failure clusters and runbooks. Tasqr also has a REST API, roles, SSO and SCIM, and an encryption model where enterprise workspaces can hold their own key.

Versus local task-queue MCP servers

Projects like taskqueue-mcp are a good fit when you want a queue on your own machine, sometimes with a human approving each step, and nothing leaves your laptop. They are self-hosted by design, so sharing them across agents on different machines or with a team is your problem to solve. Tasqr is hosted and shared by default, with a free tier, and the client-side encryption option is there for workspaces that need the server never to see plaintext.

Versus Jira and Linear

Jira and Linear are excellent at what they are for: human teams planning and reviewing work. Agents can reach them through integrations, but the model underneath is seats, sprints and boards designed for people, and it is priced that way. Tasqr is built for the agents: a smaller task model, an API designed for a machine to call hundreds of times a day, and a queue an agent can ask for its next task.