MCP GitHub Project Manager

A comprehensive Model Context Protocol (MCP) server that provides advanced GitHub project management capabilities with AI-powered task management and complete requirements traceability. Transform your project ideas into actionable tasks with full end-to-end tracking from business requirements to implementation.

Overview

This server implements the Model Context Protocol to provide comprehensive GitHub project management with advanced AI capabilities. Beyond traditional project management, it offers AI-powered task generation, requirements traceability, and intelligent project planning through GitHub's GraphQL API while maintaining state and handling errors according to MCP specifications.

🚀 What Makes This Special

• AI-Powered: Transform project ideas into comprehensive PRDs and actionable tasks using multiple AI providers

• Complete Traceability: Full end-to-end tracking from business requirements → features → use cases → tasks

• Intelligent Analysis: AI-powered complexity analysis, effort estimation, and task recommendations

• Professional Standards: IEEE 830 compliant requirements documentation with enterprise-grade change management

Table of Contents

• Overview

• Quick Start

• Key Features

• Installation

• Configuration

• Usage

  • As a command-line tool

  • Running from source with TypeScript

  • As a Node.js module

  • Integration with MCP clients

  • Installing in AI Assistants

    • Claude

    • Roocode

    • Windsurf

    • VS Code

    • Cursor

    • Using Docker

  • Troubleshooting

• Architecture

• Contributing

• License

• References

• Current Status

Quick Start

Using NPM

Using Docker

For more details on Docker usage, see DOCKER.md.

Key Features

🤖 AI-Powered Task Management

• PRD Generation (generate_prd): Transform project ideas into comprehensive Product Requirements Documents

• Intelligent Task Breakdown (parse_prd): AI-powered parsing of PRDs into actionable development tasks

• Smart Feature Addition (add_feature): Add new features with automatic impact analysis and task generation

• Task Complexity Analysis (analyze_task_complexity): Detailed AI analysis of task complexity, effort estimation, and risk assessment

• Next Task Recommendations (get_next_task): AI-powered recommendations for optimal task prioritization

• Task Expansion (expand_task): Break down complex tasks into manageable subtasks automatically

• PRD Enhancement (enhance_prd): Improve existing PRDs with AI-powered gap analysis and improvements

🎯 Enhanced Task Context Generation

• Traceability-Based Context (Default): Rich context from requirements traceability without AI dependency

• AI-Enhanced Context (Optional): Comprehensive business, technical, and implementation context using AI

• Configurable Context Levels: Choose between minimal, standard, and full context depth

• Business Context: Extract business objectives, user impact, and success metrics

• Technical Context: Analyze technical constraints, architecture decisions, and integration points

• Implementation Guidance: AI-generated step-by-step implementation recommendations

• Contextual References: Links to relevant PRD sections, features, and technical specifications

• Enhanced Acceptance Criteria: Detailed, testable criteria with verification methods

• Graceful Degradation: Works perfectly without AI keys, falls back to traceability-based context

🔗 Complete Requirements Traceability

• End-to-End Tracking (create_traceability_matrix): Full traceability from PRD business requirements → features → use cases → tasks

• Bidirectional Links: Complete bidirectional traceability with impact analysis

• Use Case Management: Professional actor-goal-scenario use case generation and tracking

• Coverage Analysis: Comprehensive coverage metrics with gap identification

• Orphaned Task Detection: Identify tasks without requirements links

• Change Impact Analysis: Track requirement changes and their impact across all levels

📊 Multi-Provider AI Support

• Anthropic Claude: Primary AI provider for complex reasoning

• OpenAI GPT: Alternative provider with fallback support

• Google Gemini: Additional AI capabilities

• Perplexity: Research and analysis tasks

• Automatic Fallback: Seamless switching between providers

🏗️ Core Project Management

• Project Management: Create and manage GitHub Projects (v2)

• Issues and Milestones: Full CRUD operations with advanced filtering

• Sprint Planning: Plan and manage development sprints with AI assistance

• Custom Fields and Views: Create different views (board, table, timeline, roadmap)

• Resource Versioning: Intelligent caching and optimistic locking

⚡ Advanced Features

• MCP Implementation: Full MCP specification compliance with Zod validation

• GitHub Integration: GraphQL API integration with intelligent rate limiting

• Real-time Sync: Bidirectional synchronization with GitHub

• Webhook Integration: Real-time updates via GitHub webhooks

• Progress Tracking: Comprehensive metrics and progress reporting

• Event System: Track and replay project events

Installation

Option 1: Install from npm (recommended)

Option 2: Install from source

Set up environment variables

Configuration

Required Environment Variables

GitHub Configuration

The GitHub token requires these permissions:

• repo (Full repository access)

• project (Project access)

• write:org (Organization access)

AI Provider Configuration

At least one AI provider is required for AI-powered features:

AI Provider Setup

Anthropic Claude

1. Sign up at Anthropic Console

2. Create an API key

3. Set ANTHROPIC_API_KEY in your environment

OpenAI

1. Sign up at OpenAI Platform

2. Create an API key

3. Set OPENAI_API_KEY in your environment

Google Gemini

1. Sign up at Google AI Studio

2. Create an API key

3. Set GOOGLE_API_KEY in your environment

Perplexity

1. Sign up at Perplexity API

2. Create an API key

3. Set PERPLEXITY_API_KEY in your environment

Usage

As a command-line tool

If installed globally:

Running from source with TypeScript

If you're developing or running from source:

Command Line Options

Command line arguments take precedence over environment variables.

As a Node.js module

Integration with MCP clients

For more examples, see the User Guide and the examples/ directory.

AI Tools Usage Examples

Complete Project Workflow

Feature Addition Workflow

Requirements Traceability

Enhanced Task Context Generation

Context Generation Levels:

• Minimal: Basic traceability context only (fastest)

• Standard: Traceability + basic business context (default)

• Full: Complete AI-enhanced context with implementation guidance

Generated Task Context Includes:

• Business Context: Why the task matters, user impact, success metrics

• Feature Context: Parent feature information, user stories, business value

• Technical Context: Constraints, architecture decisions, integration points

• Implementation Guidance: Step-by-step recommendations, best practices, pitfalls

• Enhanced Acceptance Criteria: Detailed verification methods and priorities

• Contextual References: Links to relevant PRD sections and technical specs

🧪 Testing Enhanced Context Generation

The enhanced context generation functionality includes comprehensive test coverage:

Test Files Created:

• src/__tests__/TaskContextGenerationService.test.ts - Core context generation service tests

• src/__tests__/TaskGenerationService.enhanced.test.ts - Enhanced task generation integration tests

• src/__tests__/ParsePRDTool.enhanced.test.ts - Tool-level context generation tests

Test Coverage:

• Traceability-based context generation (default behavior)

• AI-enhanced context generation (when AI is available)

• Graceful fallback when AI services are unavailable

• Configuration validation and environment variable handling

• Error handling and resilience testing

• Integration testing with existing task generation pipeline

Running Context Generation Tests:

🧪 Comprehensive E2E Testing Suite

The MCP GitHub Project Manager includes a comprehensive end-to-end testing suite that tests all MCP tools through the actual MCP interface with both mocked and real API calls.

Test Coverage:

• ✅ 40+ GitHub Project Management Tools - Complete CRUD operations for projects, milestones, issues, sprints, labels, and more

• ✅ 8 AI Task Management Tools - PRD generation, task parsing, complexity analysis, feature management, and traceability

• ✅ Complex Workflow Integration - Multi-tool workflows and real-world project management scenarios

• ✅ Real API Testing - Optional testing with actual GitHub and AI APIs

• ✅ Schema Validation - Comprehensive argument validation for all tools

• ✅ Error Handling - Graceful error handling and recovery testing

Quick Start:

Test Runner Options:

Environment Setup for Real API Testing:

GitHub API (Required for GitHub tools):

AI APIs (Required for AI tools):

Enable Real API Testing:

Test Features:

• Tool Registration Validation - Verify all tools are properly registered with correct schemas

• MCP Protocol Compliance - Ensure all tools follow MCP specification

• Response Format Validation - Validate tool responses match expected formats

• Workflow Integration Testing - Test complex multi-tool workflows

• Credential Management - Graceful handling of missing credentials

• Performance Monitoring - Track tool execution performance

• Comprehensive Error Testing - Validate error handling and recovery

Documentation:

• 📖 Comprehensive E2E Testing Guide - Detailed testing documentation

• 🔧 Test Configuration - Jest configuration for E2E tests

• 🛠️ Test Utilities - Reusable test utilities

The E2E test suite ensures that all MCP tools work correctly both individually and in complex workflows, providing confidence in the reliability and integration of the entire system.

Test Scenarios Covered:

• ✅ Default traceability-based context (no AI required)

• ✅ AI-enhanced business context generation

• ✅ AI-enhanced technical context generation

• ✅ Implementation guidance generation

• ✅ Context merging and conflict resolution

• ✅ Error handling and graceful degradation

• ✅ Configuration validation and defaults

• ✅ Tool-level parameter validation

• ✅ Integration with existing traceability system

Installing in AI Assistants

Install in Claude

To install the MCP server in Claude Desktop:

For Claude Code CLI, run:

Install in Roocode

Add this to your Roocode configuration:

Install in Windsurf

Add this to your Windsurf MCP config file:

See Windsurf MCP docs for more information.

Install in VS Code

Add this to your VS Code MCP config file:

See VS Code MCP docs for more information.

Install in Cursor

Add this to your Cursor MCP config file:

See Cursor MCP docs for more information.

Using Docker

If you prefer to run the MCP server in a Docker container:

1. Build the Docker Image:

   Create a Dockerfile in your project directory:

   FROM node:18-alpine WORKDIR /app # Install the package globally RUN npm install -g mcp-github-project-manager # Default command to run the server CMD ["mcp-github-project-manager"]

   Build the image:

   docker build -t github-project-manager-mcp .

2. Configure Your MCP Client:

   Update your MCP client's configuration to use the Docker command:

   { "mcpServers": { "github-project-manager": { "command": "docker", "args": ["run", "-i", "--rm", "github-project-manager-mcp"], "env": { "GITHUB_TOKEN": "your_github_token", "GITHUB_OWNER": "your_username", "GITHUB_REPO": "your_repo" } } } }

Troubleshooting

Common Issues

1. Module Not Found Errors

   If you encounter module resolution issues, try using bunx instead of npx:

   { "mcpServers": { "github-project-manager": { "command": "bunx", "args": ["-y", "mcp-github-project-manager"] } } }

2. Windows-Specific Configuration

   On Windows, you may need to use cmd to run the command:

   { "mcpServers": { "github-project-manager": { "command": "cmd", "args": [ "/c", "npx", "-y", "mcp-github-project-manager" ] } } }

3. Permission Issues

   If you encounter permission issues, make sure your GitHub token has the required permissions listed in the Configuration section.

Architecture

The server follows Clean Architecture principles with distinct layers:

• Domain Layer: Core entities, repository interfaces, and Zod schemas

• Infrastructure Layer: GitHub API integration and implementations

• Service Layer: Business logic coordination

• MCP Layer: Tool definitions and request handling

See ARCHITECTURE.md for detailed architecture documentation.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

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

License

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

References

• Model Context Protocol

• GitHub Projects API

• Project Roadmap

Current Status

Core Features

AI-Powered Features

Requirements Traceability

MCP Implementation

See STATUS.md for detailed implementation status. | Resource Management | ✅ Complete | With optimistic locking and relationship tracking | | Response Handling | ✅ Complete | Rich content formatting with multiple content types | | Error Handling | ✅ Complete | Comprehensive error mapping to MCP error codes | | State Management | ✅ Complete | With conflict resolution and rate limiting |

Recent Improvements

• Enhanced Resource System:

  • Added Zod schema validation for all resource types

  • Implemented resource relationship tracking

  • Created a centralized ResourceFactory for consistent resource access

• Improved GitHub API Integration:

  • Added intelligent rate limiting with automatic throttling

  • Implemented pagination support for REST and GraphQL APIs

  • Enhanced error handling with specific error types

• Advanced Tool System:

  • Created tool definition registry with Zod validation

  • Implemented standardized tool response formatting

  • Added example-based documentation for all tools

• Rich Response Formatting:

  • Added support for multiple content types (JSON, Markdown, HTML, Text)

  • Implemented progress updates for long-running operations

  • Added pagination support for large result sets

Identified Functional Gaps

Despite the recent improvements, the following functional gaps still exist and are prioritized for future development:

1. Persistent Caching Strategy:

   • While the ResourceCache provides in-memory caching, it lacks persistence across server restarts

   • No distributed caching for multi-instance deployments

   • Missing cache eviction policies for memory management

2. Real-time Event Processing:

   • No webhook integration for real-time updates from GitHub

   • Missing event-based subscription system for clients

   • Lack of server-sent events (SSE) support for streaming updates

3. Advanced GitHub Projects v2 Features:

   • Limited support for custom field types and validation

   • Incomplete integration with GitHub's newer Projects v2 field types

   • Missing automation rule management

4. Performance Optimization:

   • No query batching for related resources

   • Missing background refresh for frequently accessed resources

   • Incomplete prefetching for related resources

5. Data Visualization and Reporting:

   • No built-in visualization generators for metrics

   • Missing report generation capabilities

   • Limited time-series data analysis

See docs/mcp/gaps-analysis.md for detailed implementation status.

Documentation

• User Guide - Detailed usage instructions

• API Reference - Comprehensive tool documentation

• Tutorials - Step-by-step guides

• Examples - Code examples for common tasks

• Architecture - System architecture and design

• Contributing - Development guidelines

• MCP Documentation - MCP-specific details

Interactive Documentation

For an interactive exploration of the API, open the API Explorer in your browser.

Development

Testing

Code Quality

Contributing

We welcome contributions to the GitHub Project Manager MCP Server! Please see our Contributing Guide for details on:

• Development Workflow

• Code Standards

• Testing Guidelines

• Documentation Guidelines

License

MIT