Linear MCP Server

Linear MCP is a specialized Model Context Protocol server that bridges AI assistants with the Linear project‑management platform. By exposing a concise set of tools—create, list, and update issues, as well as query teams and projects—it enables agents to orchestrate work items directly from conversational contexts. This eliminates the need for manual API calls or UI interactions, allowing developers to embed sophisticated issue‑tracking workflows into AI‑driven applications.

The server solves a common pain point for teams that rely on Linear: automating routine task creation, status updates, and data retrieval without leaving the chat. For example, a project manager can ask an AI assistant to “create a bug ticket for the login failure” and the MCP will translate that request into a properly authenticated Linear API call. Likewise, developers can prompt the assistant to “list all open issues assigned to me in the backend team,” and receive a filtered, formatted response instantly. This tight integration streamlines communication loops and reduces context switching.

Key capabilities are presented through a minimal yet powerful tool set. Issue Management covers the full lifecycle—creating new tickets with rich metadata (title, description, priority, labels), listing existing issues with flexible filters (team, assignee, status), and updating fields such as status or assignee. Team Management exposes a lightweight enumeration of workspace teams, including identifiers and descriptions that can be used to scope issue queries. Project Management allows listing projects, optionally filtered by team, and retrieving detailed project attributes. All tools are designed to accept plain JSON payloads, making them easy to invoke from any MCP‑compatible client.

Real‑world scenarios illustrate the server’s value. In a continuous delivery pipeline, an AI bot can automatically open Linear tickets when automated tests fail, attaching relevant logs and assigning them to the responsible engineer. In agile retrospectives, a conversational agent can pull all sprint issues for discussion or generate summary reports. When onboarding new hires, the assistant can create starter tasks and assign them to appropriate teams, ensuring that work items are immediately actionable.

Integration with AI workflows is straightforward. Once the MCP server is registered in a client’s configuration (Claude Desktop, VS Code Cline extension, or Cursor), the assistant can call any of the exposed tools using standard MCP messages. The server handles authentication via a Linear API key, validates requests, and translates them into GraphQL calls to Linear. The response is then fed back into the conversation, enabling a seamless loop of intent → tool call → result. This pattern encourages developers to embed domain knowledge into AI agents without maintaining custom SDKs or handling OAuth flows.

Linear MCP stands out by offering a focused, production‑ready bridge to Linear with minimal overhead. Its clear API surface, support for rich issue metadata, and compatibility across major AI platforms make it an ideal choice for teams that want to empower agents with direct control over their project management workflow.