Feature Requests

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Feature request – Give AI tools access to Sprint Reporting (API + MCP)
Summary We use AI assistants connected to ClickUp to automate our sprint metrics, through the public API and the official ClickUp MCP server. These tools can read tasks, lists and comments. They cannot read Sprint Reporting (Sprint Task Report, and the Burndown/Burnup and Velocity reports), because that data is not exposed through the API or the MCP. We ask that it be made available to AI integrations. Current behavior The MCP server and the public API have no endpoint or tool for sprint reports. An AI assistant only sees the current state of each task. It cannot see the committed snapshot taken at sprint confirmation or the Added/Removed/Completed/Remaining breakdown that ClickUp already calculates and shows on screen. Rebuilding this data from task history gives wrong results: moving a task between lists does not update date_updated, and closed sprints cannot be recovered at all. ClickUp Brain also cannot answer questions such as "how many points were added to Sprint 27 after confirmation?" Why it matters Our product team runs an automated routine with an AI assistant that collects sprint metrics (committed vs. completed, scope added mid-sprint, carry-over) and publishes them for leadership. Sprint Reporting is the only reliable source for these numbers. Without access, the routine depends on someone copying values from the screen by hand. Requested improvement Public API endpoint(s) that return the Sprint Task Report for a given sprint: per-sprint totals, plus a per-task list with ID, name, assignee, points, status, category (Committed / Added / Removed / Completed / Remaining / After sprint close) and the date the task entered or left the sprint. This must include the committed snapshot at confirmation time. The same data exposed as tools in the official ClickUp MCP server, so assistants such as Claude, ChatGPT and Copilot can query it with the user's permissions. Velocity and Burndown/Burnup data through the same endpoints, covering several sprints at once. (Nice to have) ClickUp Brain able to answer questions using Sprint Reporting data. Acceptance criteria Given a confirmed sprint, when an AI assistant calls the API or MCP for that sprint's report, then it receives every task with its category, and the totals match the cards shown in Sprint Reporting. Given a sprint that is already closed, the response still returns the committed snapshot as it was at confirmation. The response uses the effort measure set for the report (sprint points, time estimate or task count). Access follows the same permissions as the user or token that authenticated the request. Environment Sprints ClickApp, Sprint Reporting → Sprint Task Report. Effort measure: Sprint Points. Sprint length: 2 weeks. Clients: public API v2 and the ClickUp MCP server.
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ClickUp AI
Feature request – Export data from Sprint Reporting (Sprint Task Report)
Summary The Sprint Reporting area ("Sprint Task Report") shows useful sprint data: Committed, Added, Removed, Completed, Remaining, and tasks after sprint close. However, there is no way to export it. We need to get this data out of ClickUp to track team metrics over time. Current behavior The report only shows totals (e.g., Sprint 27, 05/10/26–19/10/26: Committed 160 pts, Added 55 pts, Removed 6 pts, Completed 44 pts, Remaining 165 pts) and a task breakdown by category. The "…" menu has no option for CSV/Excel/PDF export, copy, or scheduled sending. The public API does not expose this data either. In particular, it does not give the committed snapshot taken at sprint confirmation (e.g., "Confirmed: Oct 5 at 13:00 UTC-03") or which tasks were added or removed after that point. Rebuilding this from task history is unreliable: moving tasks between lists does not update date_updated, and for older sprints the data cannot be recovered at all. Why it matters Our product team tracks planning predictability (committed vs. completed, scope added mid-sprint) across sprints. Today we have to copy numbers by hand from the screen, sprint by sprint. This is slow and error-prone, and it is impossible for sprints that are already closed if nobody wrote the numbers down. Requested improvement Export button in the Sprint Task Report (CSV and/or XLSX). It should include each task's ID, name, assignee, points, status, the category it belongs to (Committed / Added / Removed / Completed / Remaining / After sprint close), and the date it entered or left the sprint. Summary export with the totals per sprint, so we can export several sprints at once and compare them. API endpoint that returns the same report data, including the committed snapshot at confirmation time, so we can automate our metrics. (Nice to have) Scheduled export by email when a sprint closes. Acceptance criteria Given a confirmed sprint, when I click Export in the Sprint Task Report, then I get a file with every task and its category, and the totals in the file match the cards on screen. Given a sprint that is already closed, the export still returns the committed snapshot as it was at confirmation. The export follows the effort measure set for the report (sprint points, time estimate, or task count). Environment Sprints ClickApp, Sprint Reporting → Sprint Task Report. Effort measure: Sprint Points. Non-working days: Saturday and Sunday. Sprint length: 2 weeks.
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Automations
Brain CLI: authenticate and use ClickUp Brain from the terminal and agent harnesses (Claude Code, Codex, Cursor, pi, etc.)
Problem More and more of our work runs through terminal agents and wrappers: Claude Code, Codex CLI, Cursor, pi and custom orchestrators. Brain is the one assistant that actually knows our company, with workspace context, installed skills, memory, connected apps and permissions. But it only exists inside the ClickUp UI and Brain MAX. So we either keep switching between tools, or we rebuild that context badly in other agents. The ClickUp MCP server doesn't solve this. It gives another model raw access to tasks and docs. It doesn't give us Brain itself: its skills, memory, Super Agents, connected-app search, or its judgment about our workspace. Request A first-party Brain CLI and agent endpoint: Auth: brain login using OAuth or a device-code flow tied to my ClickUp account. Tokens are scoped to my existing permissions, with SSO/SAML support and admin controls. Interactive mode: a brain REPL in the terminal with the same capabilities as Brain in ClickUp: skills (system and user-installed, so new skills show up automatically), memory, search across the workspace and connected apps, and write actions using the same confirmation rules. Headless mode: something like brain -p "..." --json for scripts and CI, with streaming output and structured results. Agent interoperability: expose Brain as an MCP server and/or agent-to-agent (A2A) endpoint, so Claude Code, Codex, Cursor and other harnesses can delegate work to Brain as a sub-agent. Today they can only call low-level ClickUp tools. Super Agents: invoke or chat with workspace Super Agents from the CLI. Local context bridge (opt-in): let Brain read files from the local working directory, so it can, for example, update a task with a diff summary or check a spec against the code. Governance: every CLI action shows up in the ClickUp audit log the same way UI actions do, permissions are identical to the web app, and admins can enable or disable the CLI at workspace level. Why it matters • Brain becomes the company's context layer for every agent, not just one more chat window. • Skills and memory built in ClickUp become reusable everywhere, which makes the Brain seat worth a lot more. • Without this, teams will keep piping ClickUp data into other models, and ClickUp turns into a database rather than the intelligence layer. Example workflows • In Claude Code: "ask Brain what the acceptance criteria are for this ticket and which compliance rules apply," then implement. • A CI step: brain -p "summarise this release and post it to the #releases channel" • From a terminal: brain "what's blocking the DK launch?", answered with full workspace and connected-app context.
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ClickUp AI
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