atlassian-mcp▌
jeffallan/claude-skills · updated May 28, 2026
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Jira and Confluence automation via MCP protocol with JQL/CQL queries, ticket management, and sprint workflows.
- ›Supports querying Jira issues with JQL filters, creating and updating tickets with custom fields, and managing sprints and backlogs
- ›Enables searching, creating, and editing Confluence pages using CQL syntax with space and content management
- ›Includes authentication patterns for OAuth 2.0, API tokens, and PAT credentials with permission scope validation
- ›Provides reference g
Atlassian MCP Expert
When to Use This Skill
- Querying Jira issues with JQL filters
- Searching or creating Confluence pages
- Automating sprint workflows and backlog management
- Setting up MCP server authentication (OAuth/API tokens)
- Syncing meeting notes to Jira tickets
- Generating documentation from issue data
- Debugging Atlassian API integration issues
- Choosing between official vs open-source MCP servers
Core Workflow
- Select server - Choose official cloud, open-source, or self-hosted MCP server
- Authenticate - Configure OAuth 2.1, API tokens, or PAT credentials
- Design queries - Write JQL for Jira, CQL for Confluence; validate with
maxResults=1before full execution - Implement workflow - Build tool calls, handle pagination, error recovery
- Verify permissions - Confirm required scopes with a read-only probe before any write or bulk operation
- Deploy - Configure IDE integration, test permissions, monitor rate limits
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Server Setup | references/mcp-server-setup.md |
Installation, choosing servers, configuration |
| Jira Operations | references/jira-queries.md |
JQL syntax, issue CRUD, sprints, boards, issue linking |
| Confluence Ops | references/confluence-operations.md |
CQL search, page creation, spaces, comments |
| Authentication | references/authentication-patterns.md |
OAuth 2.0, API tokens, permission scopes |
| Common Workflows | references/common-workflows.md |
Issue triage, doc sync, sprint automation |
Quick-Start Examples
JQL Query Samples
# Open issues assigned to current user in a sprint
project = PROJ AND status = "In Progress" AND assignee = currentUser() ORDER BY priority DESC
# Unresolved bugs created in the last 7 days
project = PROJ AND issuetype = Bug AND status != Done AND created >= -7d ORDER BY created DESC
# Validate before bulk: test with maxResults=1 first
project = PROJ AND sprint in openSprints() AND status = Open ORDER BY created DESC
CQL Query Samples
# Find pages updated in a specific space recently
space = "ENG" AND type = page AND lastModified >= "2024-01-01" ORDER BY lastModified DESC
# Search page text for a keyword
space = "ENG" AND type = page AND text ~ "deployment runbook"
Minimal MCP Server Configuration
{
"mcpServers": {
"atlassian": {
"command": "npx",
"args": ["-y", "@sooperset/mcp-atlassian"],
"env": {
"JIRA_URL": "https://your-domain.atlassian.net",
"JIRA_EMAIL": "[email protected]",
"JIRA_API_TOKEN": "${JIRA_API_TOKEN}",
"CONFLUENCE_URL": "https://your-domain.atlassian.net/wiki",
"CONFLUENCE_EMAIL": "[email protected]",
"CONFLUENCE_API_TOKEN": "${CONFLUENCE_API_TOKEN}"
}
}
}
}
Note: Always load
JIRA_API_TOKENandCONFLUENCE_API_TOKENfrom environment variables or a secrets manager — never hardcode credentials.
Constraints
MUST DO
- Respect user permissions and workspace access controls
- Validate JQL/CQL queries before execution (use
maxResults=1probe first) - Handle rate limits with exponential backoff
- Use pagination for large result sets (50-100 items per page)
- Implement error recovery for network failures
- Log API calls for debugging and audit trails
- Test with read-only operations first
- Document required permission scopes
- Confirm before any write or bulk operation against production data
MUST NOT DO
- Hardcode API tokens or OAuth secrets in code
- Ignore rate limit headers from Atlassian APIs
- Create issues without validating required fields
- Skip input sanitization on user-provided query strings
- Deploy without testing permission boundaries
- Update production data without confirmation prompts
- Mix different authentication methods in same session
- Expose sensitive issue data in logs or error messages
Output Templates
When implementing Atlassian MCP features, provide:
- MCP server configuration (JSON/environment vars)
- Query examples (JQL/CQL with explanations)
- Tool call implementation with error handling
- Authentication setup instructions
- Brief explanation of permission requirements
How to use atlassian-mcp on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add atlassian-mcp
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches atlassian-mcp from GitHub repository jeffallan/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate atlassian-mcp. Access the skill through slash commands (e.g., /atlassian-mcp) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★74 reviews- ★★★★★Kabir Gill· Dec 28, 2024
Solid pick for teams standardizing on skills: atlassian-mcp is focused, and the summary matches what you get after install.
- ★★★★★Emma Anderson· Dec 28, 2024
Useful defaults in atlassian-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Pratham Ware· Dec 24, 2024
atlassian-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yusuf Jackson· Dec 24, 2024
Registry listing for atlassian-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chaitanya Patil· Dec 16, 2024
Solid pick for teams standardizing on skills: atlassian-mcp is focused, and the summary matches what you get after install.
- ★★★★★Kofi Sanchez· Dec 12, 2024
Registry listing for atlassian-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Yuki Ghosh· Dec 8, 2024
atlassian-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Luis Diallo· Dec 8, 2024
We added atlassian-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Michael Kim· Dec 4, 2024
atlassian-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yusuf Robinson· Nov 27, 2024
Solid pick for teams standardizing on skills: atlassian-mcp is focused, and the summary matches what you get after install.
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