developer-toolsproductivity

Jira & Linear

dxheroes

by dxheroes

Access and interact with Jira and Linear tickets directly in conversations—no context switching to Jira ticketing softwa

Integrates with Jira and Linear issue tracking systems to enable retrieval and interaction with tickets directly within conversations, eliminating context switching when accessing project management data.

github stars

13

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Works with both Jira and LinearNo context switching requiredBeta status - APIs may change

best for

  • / Software developers tracking project tasks
  • / Project managers reviewing ticket status
  • / Teams coordinating work across issue trackers

capabilities

  • / Retrieve Jira tickets and project data
  • / Query Linear issues and workspace information
  • / Search across tickets by status, assignee, or keywords
  • / Access ticket details, comments, and metadata
  • / Update ticket status and fields

what it does

Connects AI assistants to Jira and Linear issue tracking systems, allowing you to query and interact with tickets without leaving your conversation.

how to install

You can install Jira & Linear in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Jira & Linear is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP DevTools

License: MIT Beta Status

MCP (Model Context Protocol) DevTools is a collection of packages that enable AI assistants to interact with external tools and services through the Model Context Protocol.

✨ Highlights

  • 🔌 Seamless Integration: Connect AI assistants to external services and tools
  • 🛠 Extensible Framework: Easily create new integrations with the Model Context Protocol
  • 🔍 Powerful Interactions: Enable AI to access and manipulate data from external services
  • 📊 Robust Integrations: Comprehensive functionality for Jira and Linear
  • 🚀 Developer-Friendly: Simple setup with detailed documentation for the best developer experience

Note: This project is currently in beta (0.x.x versions). APIs may change between minor versions during the beta phase.

📦 Available Packages

PackageDescriptionStatus
@mcp-devtools/jiraJira MCP server integrationnpm version
@mcp-devtools/linearLinear MCP server integrationnpm version

🚀 Quick Start

Configuration in Cursor IDE

Jira Integration

  1. Open Cursor Settings → MCP
  2. Click "Add New MCP Server"
  3. Fill in the following details:
    • Name: Jira
    • Type: command
    • Command: env JIRA_URL=https://[YOUR_WORKSPACE].atlassian.net JIRA_API_MAIL=[YOUR_EMAIL] JIRA_API_KEY=[YOUR_API_KEY] npx -y @mcp-devtools/jira

Required Environment Variables:

  • JIRA_URL: Your Jira instance URL (e.g., https://your-company.atlassian.net)
  • JIRA_API_MAIL: Your Atlassian account email
  • JIRA_API_KEY: Your Atlassian API key (Create one here)

Linear Integration

  1. Open Cursor Settings → MCP
  2. Click "Add New MCP Server"
  3. Fill in the following details:
    • Name: Linear
    • Type: command
    • Command: env LINEAR_API_KEY=[YOUR_API_KEY] npx -y @mcp-devtools/linear

Required Environment Variables:

  • LINEAR_API_KEY: Your Linear API key (Create one in Linear app: Settings → API → Create Key)

Using Tools

Once configured, you can interact with tools through natural language commands in Cursor.

Jira Examples:

# Fetch a specific ticket
get ticket SCRUM-123

# Search for tickets
execute jql "project = SCRUM AND status = 'In Progress'"

# Get ticket details
read ticket SCRUM-123

# Create a new ticket
create ticket project=SCRUM summary="Fix login bug" description="Users can't log in" issuetype=Bug

Linear Examples:

# Get a specific issue
get_issue SS-33

# Search for issues
search_issues "priority is high" with limit 5

# Create a new issue
create_issue for team "eng" titled "Fix API response format" with description "The API is returning incorrect data format" and priority 1

# List teams
list_teams

For a complete list of available commands, refer to the package documentation:

📖 Documentation

🧩 Repository Structure

mcp-devtools/
├── core/                 # Infrastructure and utility packages
│   ├── typescript-config/  # Shared TypeScript configuration
│   └── http-client/        # HTTP client utilities
│
├── packages/             # Functional MCP server packages
│   ├── jira/               # Jira integration MCP server
│   │   └── README.md         # Package documentation
│   └── linear/             # Linear integration MCP server
│       └── README.md         # Package documentation
│
└── ...

🛠 Development

# Install dependencies
pnpm install

# Build all packages
pnpm build

# Development with auto-rebuild
pnpm dev

🤝 Contributing

Contributions are welcome! Please check our Contributing Guidelines for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

  • GitHub Issues: For bug reports and feature requests
  • Discussions: For questions and community support

Project Structure

mcp-devtools/
├── core/ # Infrastructure and utility packages
│ ├── typescript-config/ # Shared TypeScript configuration
│ └── http-client/ # HTTP client utilities
│
├── packages/ # Functional MCP server packages
│ ├── jira/ # Jira integration MCP server
│ └── linear/ # Linear integration MCP server
├── package.json # Root package configuration
└── pnpm-workspace.yaml # Workspace configuration

Development

Getting Started

This repository uses pnpm workspaces for package management. To get started:

  1. Install pnpm if you don't have it:

    npm install -g pnpm
    
  2. Install dependencies:

    pnpm install
    
  3. Build all packages:

    pnpm build
    

Development Workflow

For development with auto-rebuild:

pnpm dev

Publishing to NPM

This repository is set up with automated release management using release-please and GitHub Actions for publishing packages to npmjs.org.

Beta Status

All published packages are currently in beta status (0.x.x versions) and use the beta npm tag. During this phase:

  • Breaking changes may occur in minor version updates
  • Install the packages using: npm install @mcp-devtools/package-name@beta
  • When the project reaches stability, we will release version 1.0.0

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a workspace script:

pnpm inspector

The Inspector will provide a URL to access debugging tools in your browser.

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Conventional Commits

This project uses Conventional Commits to automate versioning and changelog generation. Please format your commit messages following this pattern:

<type>[optional scope]: <description>

[optional body]

[optional footer(s)]

Types:

  • feat: A new feature
  • fix: A bug fix
  • docs: Documentation changes
  • style: Changes that don't affect the code's meaning (formatting, etc.)
  • refactor: Code changes that neither fix bugs nor add features
  • perf: Performance improvements
  • test: Adding or fixing tests
  • chore: Changes to the build process or auxiliary tools

Examples:

feat(jira): add comment creation endpoint
fix(http-client): resolve timeout issue
docs: update README with new setup instructions

Breaking changes should be indicated by adding an exclamation mark after the type/scope and describing the breaking change in the body of the commit message:

feat!: redesign http-client API

BREAKING CHANGE: The http-client API has been completely redesigned to improve usability.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Troubleshooting

Common Issues

  1. Connection Problems

    • Ensure your API credentials are correct
    • Check network connectivity to your service instances
    • Verify URLs and workspace names
  2. Permission Errors

    • Ensure your accounts have appropriate permissions for the actions you're attempting
    • API tokens may need specific permissions enabled in your account settings
  3. Command Not Found

    • If using npx, ensure you're connected to npm registry
    • For local installations, check that your package installation was successful

For more troubleshooting help, open an issue on our GitHub repository.

Roadmap

Future development plans for MCP DevTools include:

  • Additional service integrations (GitHub, Confluence, etc.)
  • Enhanced security features
  • Support for custom authentication methods
  • Expanded querying capabilities
  • Performance optimizations

Community and Support

  • GitHub Issues: For bug reports and feature requests
  • Discussions: For questions and community support
  • Contributing: See our contributing guidelines above

We welcome feedback and contributions from the community to help improve these tools.

FAQ

What is the Jira & Linear MCP server?
Jira & Linear is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Jira & Linear?
This profile displays 30 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.630 reviews
  • Yuki Choi· Dec 28, 2024

    Useful MCP listing: Jira & Linear is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Liam Srinivasan· Dec 24, 2024

    Jira & Linear has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Kiara Brown· Dec 12, 2024

    Jira & Linear is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Oshnikdeep· Nov 23, 2024

    Jira & Linear is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Yuki Thomas· Nov 19, 2024

    Jira & Linear reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Oct 14, 2024

    Jira & Linear has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Ava Srinivasan· Oct 10, 2024

    We wired Jira & Linear into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Sakshi Patil· Sep 17, 2024

    Jira & Linear reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Hassan Rahman· Sep 17, 2024

    According to our notes, Jira & Linear benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • William Martinez· Sep 1, 2024

    Useful MCP listing: Jira & Linear is the kind of server we cite when onboarding engineers to host + tool permissions.

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