Skip to main content
Glama

MCP GitHub Project Manager

by HarshKumarSharma
npmGitHub
TypeScript
MIT License
24
OverviewInspectNewSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

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

Quick Start

Using NPM

# Install the package globally npm install -g mcp-github-project-manager # Set up your environment variables export GITHUB_TOKEN="your_github_token" export GITHUB_OWNER="your_github_username_or_organization" export GITHUB_REPO="your_repository_name" # Run the MCP server mcp-github-project-manager

Using Docker

# Build the Docker image docker build -t mcp-github-project-manager . # Run with environment variables docker run -it \ -e GITHUB_TOKEN=your_github_token \ -e GITHUB_OWNER=your_github_username_or_organization \ -e GITHUB_REPO=your_repository_name \ mcp-github-project-manager

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

# Install the package globally npm install -g mcp-github-project-manager # Or install in your project npm install mcp-github-project-manager

Option 2: Install from source

# Clone the repository git clone https://github.com/kunwarVivek/mcp-github-project-manager.git cd mcp-github-project-manager # Install dependencies npm install # or pnpm install # Build the project npm run build

Set up environment variables

# Copy the example environment file cp .env.example .env # Edit .env with your GitHub token and details

Configuration

Required Environment Variables

GitHub Configuration
GITHUB_TOKEN=your_github_token GITHUB_OWNER=repository_owner GITHUB_REPO=repository_name

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:

# Primary AI providers (at least one required) ANTHROPIC_API_KEY=your_anthropic_api_key_here OPENAI_API_KEY=your_openai_api_key_here GOOGLE_API_KEY=your_google_api_key_here PERPLEXITY_API_KEY=your_perplexity_api_key_here # AI Model Configuration (optional - uses defaults if not specified) AI_MAIN_MODEL=claude-3-5-sonnet-20241022 AI_RESEARCH_MODEL=perplexity-llama-3.1-sonar-large-128k-online AI_FALLBACK_MODEL=gpt-4o AI_PRD_MODEL=claude-3-5-sonnet-20241022 # AI Task Generation Configuration (optional) MAX_TASKS_PER_PRD=50 DEFAULT_COMPLEXITY_THRESHOLD=7 MAX_SUBTASK_DEPTH=3 AUTO_DEPENDENCY_DETECTION=true AUTO_EFFORT_ESTIMATION=true # Enhanced Task Context Generation Configuration (optional) ENHANCED_TASK_GENERATION=true AUTO_CREATE_TRACEABILITY=true AUTO_GENERATE_USE_CASES=true AUTO_CREATE_LIFECYCLE=true ENHANCED_CONTEXT_LEVEL=standard INCLUDE_BUSINESS_CONTEXT=false INCLUDE_TECHNICAL_CONTEXT=false INCLUDE_IMPLEMENTATION_GUIDANCE=false

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:

# Start the MCP server using stdio transport mcp-github-project-manager # Start with environment variables GITHUB_TOKEN=your_token mcp-github-project-manager # Start with command line arguments mcp-github-project-manager --token=your_token --owner=your_username --repo=your_repo # Use a specific .env file mcp-github-project-manager --env-file=.env.production # Show verbose output mcp-github-project-manager --verbose # Display help information mcp-github-project-manager --help

Running from source with TypeScript

If you're developing or running from source:

# Run directly with ts-node node --loader ts-node/esm src/index.ts # Run with command line arguments node --loader ts-node/esm src/index.ts --token=your_token --owner=your_username --repo=your_repo # Use the npm dev script (watches for changes) npm run dev # Display help information node --loader ts-node/esm src/index.ts --help
Command Line Options
OptionShortDescription
--token <token>-tGitHub personal access token
--owner <owner>-oGitHub repository owner (username or organization)
--repo <repo>-rGitHub repository name
--env-file <path>-ePath to .env file (default: .env in project root)
--verbose-vEnable verbose logging
--help-hDisplay help information
--versionDisplay version information

Command line arguments take precedence over environment variables.

As a Node.js module

import { Server } from "mcp-github-project-manager"; // Create and start an MCP server instance const server = new Server({ transport: "stdio", // or "http" for HTTP server config: { githubToken: process.env.GITHUB_TOKEN, githubOwner: process.env.GITHUB_OWNER, githubRepo: process.env.GITHUB_REPO } }); server.start();

Integration with MCP clients

// Example using an MCP client library import { McpClient } from "@modelcontextprotocol/client"; import { spawn } from "child_process"; // Create a child process running the MCP server const serverProcess = spawn("mcp-github-project-manager", [], { env: { ...process.env, GITHUB_TOKEN: "your_token" } }); // Connect the MCP client to the server const client = new McpClient({ transport: { type: "process", process: serverProcess } }); // Call MCP tools const result = await client.callTool("create_project", { title: "My Project", description: "A new GitHub project" });

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

AI Tools Usage Examples

Complete Project Workflow
# 1. Generate PRD from project idea generate_prd({ "projectIdea": "AI-powered task management system with real-time collaboration", "projectName": "TaskAI Pro", "author": "product-team", "complexity": "high", "timeline": "6 months", "includeResearch": true }) # 2. Parse PRD and generate tasks with traceability parse_prd({ "prdContent": "<generated PRD content>", "maxTasks": 30, "createTraceabilityMatrix": true, "includeUseCases": true, "projectId": "task-ai-pro" }) # 3. Get next task recommendations get_next_task({ "sprintCapacity": 40, "teamSkills": ["react", "node.js", "typescript"], "maxComplexity": 7, "includeAnalysis": true }) # 4. Analyze complex tasks analyze_task_complexity({ "taskTitle": "Implement real-time collaboration", "taskDescription": "Build WebSocket-based real-time collaboration with conflict resolution", "teamExperience": "mixed", "includeBreakdown": true, "includeRisks": true }) # 5. Break down complex tasks expand_task({ "taskTitle": "Build analytics dashboard", "taskDescription": "Create comprehensive analytics dashboard with AI insights", "currentComplexity": 8, "targetComplexity": 3, "includeEstimates": true, "includeDependencies": true })
Feature Addition Workflow
# Add new feature with complete lifecycle add_feature({ "featureIdea": "Advanced Analytics Dashboard", "description": "Real-time analytics with custom charts and AI-powered insights", "requestedBy": "product-manager", "businessJustification": "Increase user engagement and provide actionable insights", "targetUsers": ["project-managers", "team-leads", "executives"], "autoApprove": true, "expandToTasks": true, "createLifecycle": true }) # This automatically creates: # ✅ Business requirement analysis # ✅ Use cases with actor-goal-scenario structure # ✅ Tasks with complete traceability links # ✅ Lifecycle tracking for all tasks
Requirements Traceability
# Create comprehensive traceability matrix create_traceability_matrix({ "projectId": "task-ai-pro", "prdContent": "<PRD content>", "features": [...], "tasks": [...], "validateCompleteness": true }) # Output includes: # ✅ Business Requirements → Features → Use Cases → Tasks # ✅ Bidirectional traceability links # ✅ Coverage analysis with gap identification # ✅ Orphaned task detection # ✅ Unimplemented requirement tracking
Enhanced Task Context Generation
# Default: Traceability-based context (fast, no AI required) parse_prd({ "prdContent": "<PRD content>", "enhancedGeneration": true, "contextLevel": "standard" }) # Enhanced: AI-powered comprehensive context parse_prd({ "prdContent": "<PRD content>", "enhancedGeneration": true, "contextLevel": "full", "includeBusinessContext": true, "includeTechnicalContext": true, "includeImplementationGuidance": true }) # Performance optimized: Minimal context for speed parse_prd({ "prdContent": "<PRD content>", "enhancedGeneration": true, "contextLevel": "minimal", "includeBusinessContext": false, "includeTechnicalContext": false, "includeImplementationGuidance": false })

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:
# Run all AI-related tests (includes context generation) npm run test:ai # Run specific context generation tests npm test -- --testPathPattern="TaskContextGeneration" npm test -- --testPathPattern="enhanced" # Run all tests npm test

🧪 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:

# Run comprehensive E2E tests (mocked APIs) npm run test:e2e:tools # Run with real APIs (requires credentials) npm run test:e2e:tools:real # Use the interactive test runner npm run test:e2e:runner # Run specific test categories npm run test:e2e:tools:github # GitHub tools only npm run test:e2e:tools:ai # AI tools only npm run test:e2e:tools:workflows # Integration workflows

Test Runner Options:

# Interactive test runner with options node scripts/run-e2e-tests.js --help # Examples: node scripts/run-e2e-tests.js --real-api --github-only node scripts/run-e2e-tests.js --build --verbose --timeout 120 node scripts/run-e2e-tests.js --ai-only --real-api

Environment Setup for Real API Testing:

GitHub API (Required for GitHub tools):

GITHUB_TOKEN=ghp_your_github_token GITHUB_OWNER=your-github-username GITHUB_REPO=your-test-repository

AI APIs (Required for AI tools):

# At least one AI API key required ANTHROPIC_API_KEY=sk-ant-your-anthropic-key OPENAI_API_KEY=sk-your-openai-key GOOGLE_API_KEY=your-google-ai-key PERPLEXITY_API_KEY=pplx-your-perplexity-key

Enable Real API Testing:

E2E_REAL_API=true npm run test:e2e:tools:real

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:

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:

{ "mcpServers": { "github-project-manager": { "command": "npx", "args": ["-y", "mcp-github-project-manager"], "env": { "GITHUB_TOKEN": "your_github_token", "GITHUB_OWNER": "your_username", "GITHUB_REPO": "your_repo", "ANTHROPIC_API_KEY": "your_anthropic_api_key", "OPENAI_API_KEY": "your_openai_api_key", "GOOGLE_API_KEY": "your_google_api_key", "PERPLEXITY_API_KEY": "your_perplexity_api_key" } } } }

For Claude Code CLI, run:

claude mcp add github-project-manager -- npx -y mcp-github-project-manager
Install in Roocode

Add this to your Roocode configuration:

{ "mcpServers": { "github-project-manager": { "command": "npx", "args": ["-y", "mcp-github-project-manager"], "env": { "GITHUB_TOKEN": "your_github_token", "GITHUB_OWNER": "your_username", "GITHUB_REPO": "your_repo" } } } }
Install in Windsurf

Add this to your Windsurf MCP config file:

{ "mcpServers": { "github-project-manager": { "command": "npx", "args": ["-y", "mcp-github-project-manager"], "env": { "GITHUB_TOKEN": "your_github_token", "GITHUB_OWNER": "your_username", "GITHUB_REPO": "your_repo" } } } }

See Windsurf MCP docs for more information.

Install in VS Code

Add this to your VS Code MCP config file:

{ "servers": { "github-project-manager": { "type": "stdio", "command": "npx", "args": ["-y", "mcp-github-project-manager"], "env": { "GITHUB_TOKEN": "your_github_token", "GITHUB_OWNER": "your_username", "GITHUB_REPO": "your_repo" } } } }

See VS Code MCP docs for more information.

Install in Cursor

Add this to your Cursor MCP config file:

{ "mcpServers": { "github-project-manager": { "command": "npx", "args": ["-y", "mcp-github-project-manager"], "env": { "GITHUB_TOKEN": "your_github_token", "GITHUB_OWNER": "your_username", "GITHUB_REPO": "your_repo" } } } }

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 ErrorsIf 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 ConfigurationOn 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 IssuesIf 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

Current Status

Core Features

FeatureStatusNotes
Project Creation✅ CompleteFull support for v2 projects
Milestone Management✅ CompleteCRUD operations implemented
Sprint Planning✅ CompleteIncluding metrics tracking
Issue Management✅ CompleteWith custom fields support
Resource Versioning✅ CompleteWith optimistic locking and schema validation
Webhook Integration📅 PlannedReal-time updates

AI-Powered Features

FeatureStatusNotes
PRD Generation✅ CompleteMulti-provider AI support with comprehensive PRD creation
Task Generation✅ CompleteAI-powered parsing of PRDs into actionable tasks
Feature Addition✅ CompleteSmart feature addition with impact analysis
Task Complexity Analysis✅ CompleteDetailed AI analysis with risk assessment
Task Recommendations✅ CompleteAI-powered next task recommendations
Task Expansion✅ CompleteBreak down complex tasks into subtasks
PRD Enhancement✅ CompleteAI-powered PRD improvement and gap analysis
Requirements Traceability✅ CompleteEnd-to-end traceability matrix with coverage analysis

Requirements Traceability

FeatureStatusNotes
Business Requirements Extraction✅ CompleteExtract from PRD objectives and success metrics
Use Case Generation✅ CompleteActor-goal-scenario structure with alternatives
Traceability Links✅ CompleteBidirectional links with impact analysis
Coverage Analysis✅ CompleteGap identification and orphaned task detection
Change Tracking✅ CompleteRequirement change impact analysis
Verification Tracking✅ CompleteTest case mapping and verification status

MCP Implementation

ComponentStatusNotes
Tool Definitions✅ CompleteAll core tools implemented with Zod validation
Resource Management✅ CompleteFull CRUD operations with versioning
Security✅ CompleteToken validation and scope checking
Error Handling✅ CompleteAccording to MCP specifications
Transport✅ CompleteStdio and HTTP support

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

Interactive Documentation

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

Development

Testing

# Unit tests npm test # Integration tests npm run test:integration # End-to-end tests npm run test:e2e

Code Quality

# Lint code npm run lint # Type check npm run type-check # Format code npm run format

Contributing

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

License

MIT

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

A comprehensive server that provides AI-powered GitHub project management with task generation from requirements and complete traceability from business requirements to implementation.

  1. Overview
    1. 🚀 What Makes This Special
  2. Table of Contents
    1. Quick Start
      1. Using NPM
      2. Using Docker
    2. Key Features
      1. 🤖 AI-Powered Task Management
      2. 🎯 Enhanced Task Context Generation
      3. 🔗 Complete Requirements Traceability
      4. 📊 Multi-Provider AI Support
      5. 🏗️ Core Project Management
      6. ⚡ Advanced Features
    3. Installation
      1. Option 1: Install from npm (recommended)
      2. Option 2: Install from source
      3. Set up environment variables
    4. Configuration
      1. Required Environment Variables
      2. AI Provider Setup
    5. Usage
      1. As a command-line tool
      2. Running from source with TypeScript
      3. As a Node.js module
      4. Integration with MCP clients
      5. AI Tools Usage Examples
      6. 🧪 Testing Enhanced Context Generation
    6. 🧪 Comprehensive E2E Testing Suite
      1. Test Coverage:
      2. Quick Start:
      3. Test Runner Options:
      4. Environment Setup for Real API Testing:
      5. Test Features:
      6. Documentation:
      7. Installing in AI Assistants
      8. Troubleshooting
    7. Architecture
      1. Contributing
        1. License
          1. References
            1. Current Status
              1. Core Features
              2. AI-Powered Features
              3. Requirements Traceability
              4. MCP Implementation
              5. Recent Improvements
              6. Identified Functional Gaps
            2. Documentation
              1. Interactive Documentation
            3. Development
              1. Testing
              2. Code Quality
            4. Contributing
              1. License

                Related MCP Servers

                • mcp-github-project-manager

                  kunwarVivek
                  -
                  security
                  A
                  license
                  -
                  quality
                  Provides comprehensive tools for managing GitHub projects, milestones, tasks, and sprints. This server integrates deeply with GitHub Projects V2, offering features like automated kanban workflows, sprint planning, and custom field management.
                  Last updated -
                  24
                  31
                  TypeScript
                  MIT License

                • GitHub Projects MCP Server

                  taylor-lindores-reeves
                  A
                  security
                  A
                  license
                  A
                  quality
                  Enables users to interact with GitHub's Projects v2 API through natural language for Agile project management, supporting repository details, issue tracking, and project board management operations.
                  Last updated -
                  31
                  19
                  TypeScript
                  GPL 2.0

                • GitHub MCP Server

                  tuanle96
                  -
                  security
                  F
                  license
                  -
                  quality
                  Enables comprehensive GitHub operations through natural language including file management, repository administration, issue tracking, and advanced code searching.
                  Last updated -
                  22,573
                  1
                  TypeScript

                • GitHub MCP Server

                  renantrendt
                  -
                  security
                  F
                  license
                  -
                  quality
                  A Model Context Protocol server that enables AI assistants to perform GitHub operations including repository management, file operations, issue tracking, and pull request creation.
                  Last updated -
                  1
                  TypeScript

                View all related MCP servers

                New MCP Servers

                MCP directory API

                We provide all the information about MCP servers via our MCP API.

                curl -X GET 'https://glama.ai/api/mcp/v1/servers/HarshKumarSharma/MCP'

                If you have feedback or need assistance with the MCP directory API, please join our Discord server