iashu2k/sprintwise-jira-mcp-server

SprintWise – Jira MCP Server

Vidcast Link

For PPT please refer to the repository 'Team Titans - Presentation.pptx' Link

Team Titans

May 30, 2025

Team Members

• Ashutosh Mishra, Team Leader Software Engineer, @ashutmi2
• Lavanya Sai Kumar Kantubhukta, Software Engineer, @lkantubh
• Amal Nazar Rajeena, Software Engineer, @anazarra

Overview

SprintWise is an AI-powered Jira MCP server that revolutionizes Agile project management.

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

Features

Jira MCP Server - SprintWise

1. Optimizes Backlog Grooming
2. Smart User Story Assignment
3. Check user stories compliance
4. Identify Blockers
5. Provide insights to boost team performance
6. All CRUD operations supported.

Available Tools

1. User Management

// Get user's account ID by email
{
  email: "user@example.com";
}

2. Issue Type Management

// List all available issue types
// Returns: id, name, description, subtask status
// No parameters required

3. Issue Link Types

// List all available issue link types
// Returns: id, name, inward/outward descriptions
// No parameters required

4. Issue Management

Retrieving Issues

// Get all issues in a project
{
  projectKey: "PROJECT"
}

// Get issues with JQL filtering
{
  projectKey: "PROJECT",
  jql: "status = 'In Progress' AND assignee = currentUser()"
}

// Get issues assigned to user
{
  projectKey: "PROJECT",
  jql: "assignee = 'user@example.com' ORDER BY created DESC"
}

Creating Issues

// Create a standard issue
{
  projectKey: "PROJECT",
  summary: "Issue title",
  issueType: "Task",  // or "Story", "Bug", etc.
  description: "Detailed description",
  assignee: "accountId",  // from get_user tool
  labels: ["frontend", "urgent"],
  components: ["ui", "api"],
  priority: "High"
}

// Create a subtask
{
  parent: "PROJECT-123",
  projectKey: "PROJECT",
  summary: "Subtask title",
  issueType: "Subtask",
  description: "Subtask details",
  assignee: "accountId"
}

Updating Issues

// Update issue fields
{
  issueKey: "PROJECT-123",
  summary: "Updated title",
  description: "New description",
  assignee: "accountId",
  status: "In Progress",
  priority: "High"
}

Issue Dependencies

// Create issue link
{
  linkType: "Blocks",  // from list_link_types
  inwardIssueKey: "PROJECT-124",  // blocked issue
  outwardIssueKey: "PROJECT-123"  // blocking issue
}

Deleting Issues

// Delete single issue
{
  issueKey: "PROJECT-123"
}

// Delete issue with subtasks
{
  issueKey: "PROJECT-123",
  deleteSubtasks: true
}

// Delete multiple issues
{
  issueKeys: ["PROJECT-123", "PROJECT-124"]
}

Field Formatting

Description Field

The description field supports markdown-style formatting:

• Use blank lines between paragraphs
• Use "- " for bullet points
• Use "1. " for numbered lists
• Use headers ending with ":" (followed by blank line)

Example:

Task Overview:

This task involves implementing new features:
- Feature A implementation
- Feature B testing

Steps:
1. Design component
2. Implement logic
3. Add tests

Acceptance Criteria:
- All tests passing
- Documentation updated

Error Handling

The server provides detailed error messages for:

• Invalid issue keys
• Missing required fields
• Permission issues
• API rate limits

Setup Instructions

1. Clone the repository:

   git clone https://github.com/cisco-it-hackathon/titans-sprintwise
   cd Titans-Sprintwise
   

2. Install dependencies:

   npm install
   

3. Configure environment variables: Create a .env file in the root directory:

   JIRA_HOST=your-instance.atlassian.net
   JIRA_EMAIL=your-email@example.com
   JIRA_API_TOKEN=your-api-token
   

4. Build the project:

   npm run build
   

5. Start the server:

   npm start
   

Demonstration

• GitHub Copilot – MCP Client with LLM Claude 3.7 Sonnet
• Server is run locally (stdio) in VS Code

Future Potential

1. Intelligent Insights: Provides data-driven insights into team velocity, estimation accuracy, and feedback trends to drive continuous improvement.
2. Automated Reporting: Generates quarterly reports summarizing team performance, key achievements, blockers, and actionable recommendations, saving hours of manual effort.
3. Proactive Blocker Detection: Alerts team leads to potential risks and blockers, suggesting resolutions to keep projects on track.

Note: We weren’t able to demonstrate these features because it’s difficult to recreate it in a demo Jira account and using MCP with actual enterprise Jira is a security risk.