MCP Server for AI-powered issue analysis - issue summarization, trend analysis, priority scoring, label suggestions, and project roadmap generation
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Complete refactor with comprehensive improvements: ✨ New Features: - 19 comprehensive unit tests with Vitest - 100% TypeScript type safety - Roadmap generation capability 🏗️ Architecture: - Modular structure (types, tool-definitions, handlers, summarizer-service, index) - index.ts: 274 → 76 lines (72% reduction) 🧪 Testing: - Full test coverage 📦 Dependencies: - MCP SDK upgraded to 1.20.0 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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📊 issue-summarizer-mcp
MCP Server for automatic issue summarization and prioritization with AI - featuring LLM integration, trend analysis, and comprehensive testing.
What's New in v2.0.0
✨ Complete Refactor - Modular architecture with 72% code reduction
🧪 19 Unit Tests - Full test coverage with Vitest
🎯 100% Type Safety - Eliminated all any types
📚 Complete JSDoc - Comprehensive API documentation
📦 MCP SDK 1.20.0 - Latest SDK with improved capabilities
🗺️ Roadmap Generation - New AI-powered roadmap tool
Features
Core Features
- 📝
summarize_issue- Summarize a single issue using AI to extract key insights, category, and suggested priority - 📊
summarize_all_open- Summarize all open issues in a repository with AI-generated summaries for each - 🎯
prioritize_issues- Prioritize issues based on AI content analysis with scores and reasoning - 🏷️
suggest_labels- Suggest appropriate labels for an issue based on AI analysis of content - 📈
analyze_trends- Analyze trends and patterns across multiple issues using AI to identify common topics and recurring problems - 🗺️
generate_roadmap- Generate a project roadmap based on issues with immediate, short-term, and long-term priorities
Advanced Features
- 🤖 AI-powered categorization and priority assignment
- 📊 Trend analysis (recurring topics, common problems)
- 🔍 Multi-issue batch processing
- 🎯 Score-based prioritization (0-100)
- 📋 Structured JSON output for all operations
Installation
npm install
npm run build
Configuration
Required environment variables:
LLM_API_URL- URL to LLM API (Ollama, OpenAI, etc.)LLM_MODEL- Model name (default: llama3.2)LLM_API_KEY- API key (if required)
Optional:
FORGEJO_URL- Forgejo instance URLFORGEJO_TOKEN- Forgejo API tokenGITHUB_TOKEN- GitHub API tokenGITLAB_TOKEN- GitLab API token
Usage
export LLM_API_URL="http://localhost:11434"
export LLM_MODEL="llama3.2"
npm start
Examples
Summarize an issue
{
"tool": "summarize_issue",
"arguments": {
"issueText": "Bug: Application crashes when uploading large files...",
"includeContext": true
}
}
Prioritize issues
{
"tool": "prioritize_issues",
"arguments": {
"issues": [
{"id": 1, "title": "Critical bug", "body": "..."},
{"id": 2, "title": "Feature request", "body": "..."}
]
}
}
Suggest labels
{
"tool": "suggest_labels",
"arguments": {
"issueTitle": "Add dark mode support",
"issueBody": "Users have requested dark mode..."
}
}
LLM Integration
This MCP can work with:
- Ollama (local): Set
LLM_API_URL=http://localhost:11434 - OpenAI: Set
LLM_API_URL=https://api.openai.com/v1and provide API key - Custom LLM APIs that follow OpenAI-compatible format
License
MIT