Linear MCP Server Integration

What does “Linear” MCP Server do?

The Linear MCP Server is a Model Context Protocol (MCP) server built for seamless integration with the Linear API, enabling AI assistants and agents to interact programmatically with Linear’s issue tracking system. By exposing Linear’s core functionalities via the MCP protocol, this server empowers developers and AI tools to automate and manage project workflows, such as creating, updating, and searching issues, retrieving team information, and managing assignments and comments. The Linear MCP Server acts as a bridge between large language models and the Linear platform, streamlining common developer tasks, supporting enhanced productivity, and fostering efficient collaboration in software projects by facilitating secure, rate-limited, and controlled access to Linear’s data and actions.

List of Prompts

No prompt templates are documented in the available resources.

List of Resources

No explicit resources are documented in the available resources.

List of Tools

• linear_create_issue
  Creates a new Linear issue with specified details. Supports creating sub-issues and assigning labels.
  Parameters:

  • title (required): Issue title
  • team (required): Team identifier (key, UUID, or name)

• linear_update_issue
  Updates an existing Linear issue with new data (enabled with write access).

• linear_search_issues
  Searches for Linear issues based on specified criteria.

• linear_get_user_issues
  Retrieves all issues assigned to a specific user.

• linear_get_issue
  Fetches detailed information about a specific issue.

• linear_get_teams
  Retrieves information about teams within Linear.

• linear_add_comment
  Adds a comment to an existing Linear issue (enabled with write access).

Use Cases of this MCP Server

• Automated Issue Creation and Management
  Developers or AI agents can programmatically create, update, and comment on Linear issues to automate bug tracking, feature requests, and task workflows, improving efficiency and reducing manual overhead.

• Personalized Task Retrieval
  AI-powered assistants can fetch issues assigned to specific users, helping developers quickly identify and prioritize their work items within Linear.

• Project Team Insights
  Teams can utilize the server to retrieve and analyze team composition and responsibilities, supporting better project planning and resource allocation.

• Integrating Linear with DevOps Workflows
  By exposing Linear’s API via MCP, the server enables integration with CI/CD pipelines, chatbots, or dashboards, facilitating real-time project monitoring and automated status updates.

• Enhanced Collaboration via AI Assistants
  AI assistants can interact with Linear through the MCP server to summarize project status, recommend next actions, or surface relevant issues during team meetings.

How to set it up

Windsurf

No specific instructions or configuration available for Windsurf.

Claude

No specific instructions or configuration available for Claude.

Cursor

No specific instructions or configuration available for Cursor.

Cline

1. Prerequisites:

   • Ensure Go 1.23 or higher is installed.
   • Obtain your Linear API key.

2. Installation:

   • Download the appropriate binary from GitHub Releases.
   • Make it executable:
     chmod +x linear-mcp-go-*

3. Set API Key:

   • Export your Linear API key:
     export LINEAR_API_KEY=your_linear_api_key

4. Run Setup:

   • Run the setup for Cline:
     ./linear-mcp-go setup
   • To enable write access:
     ./linear-mcp-go setup --write-access
   • To auto-approve read-only tools:
     ./linear-mcp-go setup --auto-approve=allow-read-only

5. Verify:

   • The setup process checks for installation, configures the AI assistant, and sets up auto-approval for specified tools.

Example JSON configuration for Cline

{
  "mcpServers": {
    "linear-mcp-go": {
      "command": "./linear-mcp-go",
      "args": ["serve"]
    }
  }
}

To enable write access:

{
  "mcpServers": {
    "linear-mcp-go": {
      "command": "./linear-mcp-go",
      "args": ["serve", "--write-access"]
    }
  }
}

Securing API Keys

Set your API keys using environment variables:

export LINEAR_API_KEY=your_linear_api_key

Or within the config:

{
  "mcpServers": {
    "linear-mcp-go": {
      "command": "./linear-mcp-go",
      "args": ["serve"],
      "env": {
        "LINEAR_API_KEY": "your_linear_api_key"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "linear-mcp-go": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “linear-mcp-go” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above, the Linear MCP server is a focused, well-documented integration for Linear workflows, but lacks advanced MCP features such as prompt templates, explicit resource primitives, or sampling/roots documentation. It provides the essentials for developers wanting to automate or enhance their Linear experience via AI assistants.

MCP Score