ClickUp MCP
Connect AI agents to ClickUp over MCP to read, create, and move work
A Model Context Protocol integration lets autonomous AI agents operate directly inside ClickUp — reading tasks, docs, and custom fields, creating and updating work, and moving items through statuses. Instead of a human translating decisions into clicks, agents query the workspace for context and take structured actions against it. We build these integrations so ClickUp becomes an operational surface agents can safely act on within defined guardrails.
Query tasks, subtasks, Docs, comments, and custom-field values across Lists and Spaces to ground the agent in current delivery state.
Open tasks and subtasks, set assignees, due dates, priorities, and custom fields, and edit descriptions from generated briefs or upstream triggers.
Transition tasks across statuses, apply tags, and trigger the automations attached to those changes to advance the pipeline.
Post updates, summaries, and requests as task comments so agent actions stay visible and auditable to the human team.
An agent reads inbound requests, classifies them, and creates properly structured ClickUp tasks — correct List, custom fields, priority, and owner — so nothing sits unrouted in an inbox.
On a schedule, an agent queries open tasks and recent status changes across client Spaces and posts a concise, per-account progress summary with flagged blockers to the right task or Doc.
When a campaign or content plan is approved, an agent decomposes it into ClickUp tasks with dependencies, assignees, and dates, then moves each into the production queue as prerequisites clear.
We scope the MCP connection to specific Spaces and actions, gate write operations behind rules and approvals for sensitive steps, and have agents post their actions as comments so every change is traceable and reversible.
Reading tasks, Docs, and custom fields; creating and updating tasks and subtasks; setting fields, assignees, and dates; changing statuses; and posting comments. We enable only the subset each workflow needs.
No — it complements them. Native Automations handle deterministic rules, while agents handle the judgment-based work like triage, summarization, and decomposition, then hand off to Automations by changing status or fields.
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