· 3 min read Posted by Kevin Galligan
Linear MCP Integration for AI Agents
Key takeaway: Manage Linear issues directly from your AI agent using our improved MCP integration, streamlining workflows and enhancing issue detail with minimal effort.
Install the Linear MCP with Cursor, Goose, Cline, Claude Code, Copilot (VSCode), Windsurf, and other tools that support MCP. See touchlab/linear-mcp-integration for details.
If your AI tool of choice does not support MCP, we suggest you provide them with feedback :)
Get started quickly:
- Improved Integration: We’ve published a stabilized and enhanced Model Context Protocol (MCP) integration for Linear, building upon existing work.
- Less Effort: Let your AI agent create detailed, structured issues without context switching.
- Improved Workflow: Configure team, project, status, and assignee preferences once for consistent issue creation.
- Open Source: Find our enhanced version on GitHub and NPM.
Go deeper:
The Model Context Protocol (MCP) is a standardized way for AI models and developer tools (like your IDE or issue tracker) to communicate. It allows an AI agent to understand the context of your work (files open, code you’re editing) and interact with external tools seamlessly. See the MCP Overview for more details.
We decided to enhance and publish an MCP integration for Linear because existing public options, while representing significant initial effort, were often incomplete or unstable in practice. Many seemed like experiments lacking features or proper error handling.
Our Enhanced Linear MCP Tool:
- Forked & Fixed: Building on the substantial groundwork from an initial implementation by Cline, we addressed practical issues with authentication (simplifying to PAT-based access), error reporting, and call functionality. The original tool sometimes failed silently or had configuration mismatches (e.g., assuming OAuth when PAT was configured).
- Focus on Stability & Maintenance: We aimed to provide a more robust and actively maintained tool, recognizing that many early MCP implementations suffer from limited testing and follow-through after initial creation.
- Essential Features: Create issues with specified teams, projects (a crucial feature often missing), priorities, and assignees.
- Why Use This Version?
- Contextual Awareness: The agent understands your coding context, leading to richer issue descriptions.
- Structured Output: LLMs excel at turning thoughts into well-organized text.
- Efficiency: Avoid manual copy-pasting of code snippets, file paths, or links. The agent handles it.
Future Potential:
We envision further expanding the tool’s capabilities:
- Issue Searching: Help agents find existing issues to prevent duplicates.
- Issue Updates: Allow agents to modify existing issues or add comments.
- OAuth Support: While Personal Access Tokens (PAT) are simpler for individual setup, OAuth could streamline team adoption and access control (contributions welcome!).
This integration demonstrates the power of MCP in bridging the gap between AI agents and the tools developers use daily, making workflows like issue tracking significantly more efficient.