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Linear Issue Tracker MCP Server

Developer ToolsGo
Interact with Linear issue tracking system through MCP
Available Tools

linear_search_issues

Search for Linear issues using various criteria such as text, state, assignee, etc.

linear_get_user_issues

Retrieve issues assigned to a specific user

linear_get_issue

Get detailed information about a specific Linear issue

linear_get_issue_comments

Retrieve comments for a specific Linear issue

linear_create_issue

Create a new issue in Linear with title, description, and other attributes

linear_update_issue

Update an existing Linear issue's attributes

linear_add_comment

Add a comment to a Linear issue or comment thread

linear_get_teams

Retrieve information about teams in the Linear workspace

Linear MCP Server provides a comprehensive interface to the Linear issue tracking system through the Model Context Protocol. It enables AI assistants to create, update, and search issues, add comments, retrieve team information, and more - all while respecting Linear's API rate limits. This integration bridges the gap between AI assistants and your team's project management workflow.

Overview

The Linear MCP Server allows AI assistants to interact with your Linear issue tracking system, enabling seamless integration between your AI tools and project management workflow.

Prerequisites

Before installing the Linear MCP Server, ensure you have:

  1. A Linear account and API key
  2. Go 1.23 or higher (if building from source)

Installation Options

From Pre-built Binaries

The easiest way to install is by downloading a pre-built binary from the GitHub Releases page:

  1. Download the appropriate binary for your platform (Linux, macOS, or Windows)
  2. Make it executable (on Linux/macOS):
    chmod +x linear-mcp-go-*
    

Automated Installation

You can use this script to automatically download and install the latest release:

# Download linux binary for the latest release
RELEASE=$(curl -s https://api.github.com/repos/geropl/linear-mcp-go/releases/latest)
DOWNLOAD_URL=$(echo $RELEASE | jq -r '.assets[] | select(.name | contains("linux")) | .browser_download_url')
curl -L -o ./linear-mcp-go $DOWNLOAD_URL
chmod +x ./linear-mcp-go

Configuration

Setting Up Your Environment

  1. Set your Linear API key as an environment variable:
    export LINEAR_API_KEY=your_linear_api_key
    

Running the Server Manually

You can run the server directly with:

# Run in read-only mode (default)
./linear-mcp-go serve

# Run with write access enabled
./linear-mcp-go serve --write-access

Automated Setup for AI Assistants

The setup command automates the installation and configuration process:

# Basic setup for Cline (default)
./linear-mcp-go setup

# Setup with write access enabled
./linear-mcp-go setup --write-access

# Setup with auto-approval for read-only tools
./linear-mcp-go setup --auto-approve=allow-read-only

# Setup with specific tools auto-approved
./linear-mcp-go setup --auto-approve=linear_get_issue,linear_search_issues

The --auto-approve flag can be configured with:

  • allow-read-only: Auto-approves all read-only tools
  • A comma-separated list of specific tools to auto-approve

Usage with AI Assistants

Once configured, your AI assistant can use the Linear MCP Server to:

  • Search for issues using various criteria
  • Create new issues with titles, descriptions, and assignments
  • Update existing issues
  • Add comments to issues
  • Retrieve information about teams and users

The server handles authentication and rate limiting automatically, ensuring your Linear API usage remains within acceptable limits.

Troubleshooting

If you encounter issues:

  1. Verify your Linear API key is correctly set
  2. Check that the binary has execute permissions
  3. Ensure your Linear account has appropriate permissions for the operations you're attempting
  4. Look for error messages in the server output for specific API issues

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About Model Context Protocol

Model Context Protocol (MCP) allows AI models to access external tools and services, extending their capabilities beyond their training data.

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