Feature Requests

I’d love to see a feature in ClickUp AI similar to ChatGPT’s “Custom Instructions.” Instead of re-selecting a saved prompt every time, I want to define my AI style, tone, goals, and contextual preferences once—and have them automatically applied across all interactions with ClickUp Brain and AI Agents (in tasks, chat, etc.). Why this matters: • It’s key for creative brainstorming and maintaining consistent tone across work. • Saves time from having to reapply prompts. • Makes AI truly feel like an extension of my workflow and voice. Bonus feature idea: Allow workspaces to upload contextual documents (PDFs, strategy docs, style guides) that AI can reference persistently.

Problem: The current AI Usage dashboard provides a high-level overview but lacks the necessary granularity for effective resource management. Currently, while Super Agents have a clear breakdown, other high-consumption features like Autopilot Agents, AI Cards, AI Fields, and AI Tasks only show aggregate costs. Furthermore, the dashboard prioritizes monetary value (USD) over the actual credit count, and lacks historical month-over-month reporting for trial/bonus pools. Proposed Solution: I suggest a comprehensive update to the AI Usage interface to include: Dual Metric Visibility: Display "Credits Consumed" alongside "Monetary Cost (USD)" as standard columns. Relying on tooltips (hovering) makes it difficult to compare usage across different workflows quickly. Granular Breakdown for All AI Features: Extend the "Super Agent" level of detail to Autopilot Agents, AI Fields, and AI Cards. We need to see: Which specific Agent/Automation triggered the usage. The location (List/Folder/Space) of the trigger. The User/Member responsible for the action. Historical Data & Period Filtering: Ability to filter usage by previous months or custom date ranges. Currently, the breakdown resets on the 1st of each month, making it impossible to audit past consumption patterns once the month ends. Audit Log Integration: A dedicated "AI Audit Log" or better filtering in the existing Automation Audit Log to specifically isolate AI-related consumption events. Why this is important: As a decision-maker, I cannot confidently purchase additional AI Credit packs or add-ons without understanding exactly how our current credits are being spent. Clear visibility is the bridge between a "trial" and a "paid" commitment. Better governance tools will help teams optimize their workflows and scale their AI usage responsibly.

I would like to be able to have my Super Agent create a folder and an associated list. Today it can only create a lists.

Been playing around with ClickUp Brain and wanted to throw out some thoughts, especially on how it works with tasks. I love the idea of having everything in one place, but right now the connection feels a little...clunky. What I like: ClickUp Brain as a central spot for info is awesome. The channels, search, and the promise of AI are super cool. The thing that's bugging me, though: It's the lack of easy finding and automatic linking between Brain chats and tasks. What I'd really love to see is: A dedicated brain chats area within each task to collect all the chats where I referenced this task. A real-time conversation, specifically for that task. Comments are okay, links to outside brainstorming chats are fine but the final refinement and interlinking chats should remain connected to task in clickup, but this would feel so much more modern. This will help with being able to grab a key decision from a chat and instantly drop it into the task description or a related doc. HUGE for keeping things clear while maintaining context from the chat. Easier ways to link entire chat to a task for better knowledge-sharing if required (if chat is shared too) Currently every thing with AI is of no use as it will eventually get lost in the history forever.

It'd be dope if clickup AI would support https://modelcontextprotocol.io/ . With that its agent could instantly integrate with a ton of tools without you having to do first party integrations in the first place. Some tools like cursor.ai just allow you to paste in an MCP link and bam, you have it in your agent. Support this ONE thing and save time integrating all the others. Let your users do the job and give them the power to integrate properly.

The current ClickUp MCP server ( mcp.clickup.com ) does not expose a tool to create tasks from existing task templates. The REST API has this endpoint (POST /api/v2/list/{listId}/taskTemplate/{templateId}) but it is not available as an MCP tool. Requesting either: 1. A new MCP tool clickup_create_task_from_template that wraps this endpoint, or 2. Confirmation if this is already supported and how to use it

monitor usage by individual team members or obtain detailed activity logs (ai usage, automation usage, etc)

I previously used Motion. The AI note-taker had this for the meetings. That said, creating a branding version of it for ClickUp would be wonderful. I've attached my desired result.

We would like to request enhanced administrative controls and visibility for AI credit usage within ClickUp. AI Credit Usage Dashboard Provide an admin-level dashboard that displays: Total AI credits available to the workspace. Remaining AI credits for the current billing period. AI credit consumption per user. Historical usage trends by user and by month. A ranking or breakdown showing which users are consuming the most and least AI credits. Visibility into which AI features are consuming credits (e.g., AI Chat, AI Agents, AI Automations, AI Fields, Image Generation, etc.). This would help administrators monitor adoption, identify heavy users, and forecast future AI credit requirements. Per-User AI Credit Allocation Allow workspace administrators to assign custom monthly AI credit limits on a per-user basis. For example: User 1: 1,000 AI credits per month User 2 (Creative/Marketing): 10,000 AI credits per month User 3 (Project Manager): 2,500 AI credits per month This would enable organizations to allocate resources based on job function and business needs rather than applying the same allowance to all users. Shared Pool Management If a workspace receives a pooled allocation of AI credits (for example, 70 users × 5,000 credits = 350,000 AI credits per month), provide administrators with the ability to: View the total shared credit pool. Allocate credits from the pool to specific users. Reallocate unused credits between users. Reserve credits for specific departments or teams. Track how much of the pool has been consumed and how much remains. Dynamic Credit Adjustments During the Billing Cycle Allow administrators to increase or decrease a user's AI credit allocation at any time during the month. Example: User 1 is assigned 1,000 credits per month. User 1 reaches the limit 10 days before month-end. The administrator decides to grant an additional 2,000 credits. In this scenario, the additional credits should be deducted from the workspace's remaining shared credit pool, provided sufficient credits are available. This would give organizations flexibility to support users with temporary increases in AI usage without requiring plan changes or manual intervention from ClickUp support. Business Value These capabilities would provide organizations with: Better AI cost governance. Improved budget forecasting. Fair and role-based AI resource allocation. Increased visibility into AI adoption and usage patterns. Greater administrative control over pooled AI credit consumption. This functionality would be particularly valuable for larger workspaces where AI usage requirements vary significantly between departments and individual users.

We have custom fields that are only visible and editable by some team members. We want to have AI agents editing those fields, but without making them editable to everyone in the space. Currently, we need to make it available to everyone so that the AI agent can also update it. Could we add permissions for AI agents so that I can allow my AI agent to edit a field, without making it readable/editable by the whole space? This is currently blocking us from using AI agents effectively