If you're searching for "Linear MCP", you're asking one of two things: does Linear have an MCP server? or how do I connect Linear to an AI assistant via the Model Context Protocol?
✅ Linear has an official MCP server. Details in the setup section below.
What Is MCP?
Model Context Protocol (MCP) is an open standard developed by Anthropic that lets AI assistants — like Claude — connect to external tools, APIs, and data sources in a standardised way.
Before MCP, every AI integration required bespoke tooling: custom prompts, custom API wrappers, and custom glue code to pass context back and forth. MCP replaces that with a common interface: the AI asks the MCP server for data or actions, the server returns structured results, and the AI uses them to answer your question or complete a task.
In plain terms: MCP is how you give an AI assistant live access to Linear — not just knowledge about it, but real, up-to-date data from your account.
What a Linear MCP Integration Does
Once Linear is connected via MCP, your AI assistant can:
- Read live data — pull records, metrics, activity, and status directly from Linear
- Take actions — create, update, or log records based on your instructions
- Cross-reference context — combine Linear data with other connected tools mid-conversation
The key difference from a standard chatbot: the assistant is not working from training data or memory. It is reading your actual Linear instance, in real time.
Practical Linear MCP Use Cases
Query issues and PRs without leaving chat
Ask the assistant to list open issues by label, summarise a PR, or find tickets assigned to a specific person — it queries Linear directly and returns the result.
Write tickets from conversation
Turn a Slack message or call note into a properly structured ticket in Linear: title, description, acceptance criteria, and priority — without touching the UI.
On-call context lookup
During an incident, have the assistant pull recent deployments, open issues, and related PRs from Linear to help narrow down root cause faster.
Release note generation
At the end of a sprint, ask the assistant to fetch merged PRs from Linear and produce user-facing release notes grouped by theme.
How to Connect Linear via MCP
There are two main paths:
Option A: Use Linear's official MCP server
Linear maintains an official Linear MCP server. This is the recommended starting point — it's built and maintained by the Linear team, so it stays up to date with API changes.
What the server exposes:
- issues
- projects
- cycles
- roadmap
- teams
- documents
- comments
What you'll need:
- An MCP-compatible client (Claude Desktop, OpenClaw, or another host)
- Linear credentials configured per the server's setup guide
This path gives you the most control but requires you to handle client configuration and credential management yourself.
Option B: Use Cody (OpenClaw-based, managed)
Cody is built on OpenClaw and supports MCP-compatible integrations out of the box. You connect Linear once from the Cody dashboard — no server to run, no code to write — and Cody handles authentication, context passing, and write-back actions with appropriate guardrails.
Cody works where your team already operates: Slack, Telegram, or the web chat. The Linear connection is available to your entire team without each person setting up their own MCP client.
Want Linear Connected to AI Without Running Your Own MCP Server?
Cody has Linear integration built in. Query issues, cycles, and team workload from Slack — no GraphQL proxy required.
MCP vs Other AI Integration Patterns
| Approach | What it is | Tradeoff |
|---|---|---|
| MCP | Standardised protocol for live tool access | Requires an MCP server; most powerful when set up correctly |
| RAG (retrieval) | Pre-index Linear content and retrieve it | Good for static docs; not suitable for live/transactional data |
| Manual copy-paste | Paste Linear output into ChatGPT/Claude | Fast to start; breaks for anything recurring or at scale |
| Custom API wrappers | Bespoke integration code per tool | Full control; high maintenance overhead |
MCP wins when you need live data from Linear and want to avoid rebuilding integrations as APIs change.
Common Mistakes
- Using training data when live data is needed — if the AI doesn't have an MCP connection, it will answer from memory, which is often outdated or wrong for account-specific questions
- No write-back guardrails — MCP can write to Linear, so it's worth adding an approval step for any action that modifies records
- Too many tools exposed at once — give the AI access to the Linear actions it actually needs; a scoped connection is easier to reason about and audit
- Skipping structured outputs — ask the AI to return structured JSON or clear fields when writing back to Linear; free-form output is harder to validate
Related MCP Guides
Want the full workflow picture? See: Linear AI Automation and How to Connect Linear to OpenClaw.