Linear's official MCP server launched in May 2025. It lets the robots lean in on the project management work. I want to get some ideation-bots going.

Supported Commands

Category	Commands
Issues	list, get, create/update, status, statuses
Projects	list, get, create/update, labels
Documents	list, get, create, update, search
Comments	list, create/update, delete
Attachments	get, create, delete, extract images
Milestones	list, get, create/update
Cycles	list
Teams	list, get
Users	list, get
Labels	list issue labels, create issue label

Setup

Linear hosts the MCP server at https://mcp.linear.app/mcp. Use that endpoint, not the old SSE one which is deprecated.

Claude Code

claude mcp add --transport http linear-server https://mcp.linear.app/mcp

Restart and authenticate via OAuth.

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Idea: Nightly Ideation Engine

Instead of using Linear as a human-operated task tracker, an AI agent can read youri codebase, compare it against goals, and file suggestions directly.

What I'm hoping to build is a system where a scheduled agent runs nightly. It reads blog posts, checks page speed, reviews SEO metadata, and compares everything against a set of high-level goals:

• 100% accurate content
• Fast loading
• Low cost
• Looks good
• Good SEO
• Interesting tutorials about emerging tooling

It should create Linear issues for anything worth improving, labeled "Suggestion" and drop them into the Backlog.

The pipeline

Linear's built-in statuses act as gates between human and agent work.

stateDiagram-v2
    [*] --> Backlog: AI files suggestion
    Backlog --> Todo: Human promotes
    Backlog --> Canceled: Human rejects
    Todo --> InProgress: Agent picks up
    InProgress --> InReview: Agent finishes
    InReview --> Done: Human approves
    InReview --> InProgress: Human pushes back

Each step has a clear owner:

Status	Owner	What happens
Backlog	AI	Nightly agent creates suggestion issues
Todo	Human	You move the good ones forward
In Progress	Agent	A working agent picks it up
In Review	Agent	Agent finishes, leaves a comment
Done	Human	You verify and close
Canceled	Human	Bad idea, toss it

Agent notes via comments

When agents work on an issue, they leave comments. This beats internal context files because comments are visible, searchable, and survive across sessions. If one agent starts a task and another finishes it, the full history is right there on the issue.

I might also run multiple planning agents that each comment on a ticket before I review it. A fact-checker, a SEO reviewer, and a style auditor could all weigh in on the same Backlog issue. By the time I look at it, there's already a thread of perspectives.

# An agent checking for work:
# list_issues with label="Suggestion", state="Todo"

# An agent leaving notes:
# save_comment with issueId and markdown body

Filtering

The nightly agent queries existing Backlog suggestions before creating new ones, avoiding duplicates. Working agents filter by label and status to find their next task:

list_issues → label: "Suggestion", state: "Todo"

Defining State as Prompts

Linear's community Terraform provider exists but only covers teams, labels, workflow states, and templates. No custom views. The GraphQL API has a customViewCreate mutation, but nobody's wrapped it in IaC tooling yet.

So instead of declaring views in HCL, I described mine as a prompt and had Claude set it up via Playwright MCP:

Create a custom view called "Suggestion Backlog" on the Pericak team. Filter to issues where label includes "Suggestion" and status is "Backlog". Description: "AI-generated improvement suggestions awaiting human review".

Claude navigated to Linear in Chromium, clicked "Add new view", filled in the name, added both filters, and hit Save. The view now lives as a tab on the team page.

This is nondeterministic state management. There's no lock file, no plan/apply, no drift detection. But it works, and the prompt is the documentation. If the view ever gets deleted, re-run the prompt.

A Linear-Admin agent

A "Linear-Admin" agent could own all workspace configuration: views, labels, workflow states, notification settings. You'd feed it a doc describing your desired workspace setup, and it would use Playwright to converge Linear's UI to match. Like Terraform, but with a browser instead of an API.

The tradeoffs are real. Playwright clicks are slow, brittle if Linear redesigns their UI, and hard to make idempotent. But for a personal workspace where the alternative is "click around in the UI manually," the prompt-as-IaC approach is surprisingly practical.

Still faster than a person.