Linear MCP Server

The Linear MCP Server bridges the gap between AI assistants and the Linear project‑management platform by exposing a set of intuitive, queryable tools that mirror core Linear API endpoints. Developers can now ask an AI assistant to pull real‑time task lists, drill down into individual issue details, or enumerate teams and users—all without leaving the conversational context. This eliminates the need to manually export data from Linear or write custom integration scripts, enabling rapid prototyping and seamless workflow automation.

At its core, the server offers four primary tools: , , , and . The tool accepts optional filters such as status, assignee, team, and a limit on returned items, allowing the AI to retrieve precisely the slice of work that matters. provides a deep dive into any task by ID, returning rich metadata like description, comments, and attachments. The and tools supply lightweight listings of all teams and users in the workspace, which can be used to populate dropdowns or validate references in downstream processes. Each tool is designed for minimal latency and robust error handling, ensuring that the assistant can reliably fetch or update information in a production environment.

Use cases abound for teams already embedded in Linear. Product managers can ask the assistant to “Show me all tasks assigned to the QA team that are still open,” while engineers might request a quick summary of their current sprint backlog. Support staff can pull the latest task details to surface on customer portals, and project leads can generate real‑time dashboards of team capacity. Because the server communicates via standard MCP conventions, any Claude or other MCP‑compliant client can invoke these tools with a single call, keeping the interaction declarative and easy to audit.

Integration into existing AI workflows is straightforward. The server can be deployed as a standalone Node.js service or containerized for cloud environments, and its configuration file accepts a Linear API key that grants scoped access to the workspace. Once running, developers embed tool calls into prompts or scripts, allowing the assistant to chain data retrieval with natural language reasoning. For example, an AI could automatically generate a weekly status report by first fetching tasks, then summarizing their progress, and finally sending the output to a Slack channel—all orchestrated through MCP calls.

What sets Linear MCP apart is its focus on developer ergonomics and security. By abstracting away authentication details, the server ensures that only authorized keys can access Linear data, while still exposing a clean, typed interface for tool parameters. The ability to filter tasks on the server side reduces payload size and improves response times, which is critical for latency‑sensitive conversational agents. Moreover, the modular design means new Linear endpoints can be added with minimal code changes, keeping the MCP server future‑proof as Linear evolves.