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Status](https://sonarcloud.io/api/project_badges/measure?project=gilbarbara_agent-hub-mcp&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=gilbarbara_agent-hub-mcp)\n\n**Universal AI agent coordination platform** - Enable any MCP-compatible AI assistant to collaborate across projects and share knowledge seamlessly.\n\n## Why Agent Hub MCP?\n\n**The Problem**: AI coding assistants work in isolation. Your Claude Code agent can't share insights with your Cursor agent. Knowledge remains trapped in individual sessions, and agents struggle to coordinate on complex, multi-service projects.\n\n**The Solution**: Agent Hub MCP creates a universal coordination layer that enables any MCP-compatible AI agent to communicate, share context, and collaborate—regardless of the underlying AI platform or project location.\n\n```\n┌─────────────┐    ┌─────────────────┐    ┌─────────────┐\n│ Claude Code │───▶│ Agent Hub MCP   │◀───│   Qwen      │\n│  (Frontend) │    │     (MCP)       │    │ (Backend)   │\n└─────────────┘    └─────────────────┘    └─────────────┘\n                           ▲\n                           │\n                    ┌─────────────┐\n                    │   Gemini    │\n                    │ (Templates) │\n                    └─────────────┘\n```\n\n## What You Get\n\n- 🤖 **Universal Compatibility**: Works with ANY MCP-compatible AI agent - no vendor lock-in\n- ⚡ **Minimal setup**: One-line configuration, no complex installation required  \n- 🔄 **Multi-Agent Collaboration**: Agents communicate across different platforms and projects\n- 🧠 **Shared Intelligence**: Knowledge and context flows between agents automatically\n- 📋 **Smart Coordination**: Agents track dependencies and coordinate complex multi-service tasks\n- 💾 **Persistent Memory**: All collaboration history preserved across sessions\n\n## Quick Start (5 minutes)\n\n### Step 1: Add Agent Hub MCP to Your AI Assistant\n\nFor **Claude Code**, **Qwen**, **Gemini** (JSON config):\n```json\n{\n  \"mcpServers\": {\n    \"agent-hub\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"agent-hub-mcp@latest\"]\n    }\n  }\n}\n```\n\nFor **Codex** (TOML config):\n```toml\n[mcp_servers.agent-hub]\ncommand = \"npx\"\nargs = [\"-y\", \"agent-hub-mcp@latest\"]\n```\n\n### Step 2: Install Custom Commands (Recommended)\n\nCustom commands make collaboration much easier. Install them for your AI assistant:\n\n**For Claude Code:**\n```bash\ngit clone https://github.com/gilbarbara/agent-hub-mcp.git /tmp/agent-hub-mcp\nmkdir -p ~/.claude/commands/hub\ncp /tmp/agent-hub-mcp/commands/markdown/*.md ~/.claude/commands/hub/\n```\n\n**For Qwen/Gemini:**\n```bash\ngit clone https://github.com/gilbarbara/agent-hub-mcp.git /tmp/agent-hub-mcp\nmkdir -p ~/.qwen/commands/hub  # or ~/.gemini/commands/hub\ncp /tmp/agent-hub-mcp/commands/toml/*.toml ~/.qwen/commands/hub/\n```\n\nThis enables slash commands for:\n\n- `/hub:register` (join the hub)\n- `/hub:sync` (check for messages and workloads)\n-  `/hub:status` (view hub activity)\n\n### Step 3: Restart Your AI Assistant\nClose and reopen your AI assistant completely for changes to take effect.\n\n### Step 4: Verify Installation\n\n**Register your agent:**\n```bash\n/hub:register\n```\nYou should see: `✅ Registered with Agent Hub as [your-project-name]`\n\n**Without Custom Commands:**\nAsk your AI assistant: \"Register with the Agent Hub\" then \"Check the Hub status\"\nExpected response: Confirmation that you're registered and connected\n\n**Troubleshooting Verification:**\n- ❌ No response → Check MCP server configuration and restart AI assistant\n- ❌ Connection error → Verify `npx -y agent-hub-mcp@latest` command\n- ❌ Commands not found → Ensure custom commands are installed in the correct directory\n\n✅ **Success!** You should see Agent Hub MCP status information. You're ready to collaborate!\n\n### 📬 Automatic Message Notifications (Optional)\n\nSet up automatic notifications when other agents send you messages by adding a hook to your Claude Code settings:\n\n```json\n{\n  \"hooks\": {\n    \"Stop\": [\n      {\n        \"hooks\": [\n          {\n            \"type\": \"command\",\n            \"command\": \"npx -y agent-hub-mcp-checker\"\n          }\n        ]\n      }\n    ]\n  }\n}\n```\n\nThis will automatically check for unread messages after each command and display: `📬 You have X unread messages from other agents. Type '/hub:sync' to check.`\n\n\n\n## 🤖 Works With Any MCP-Compatible AI Agent\n\nAgent Hub MCP uses the Model Context Protocol (MCP) standard, making it compatible with any AI assistant that supports MCP:\n\n### ✅ **Verified Compatible (manually tested)**\n- **Claude Code** - Primary platform, thoroughly tested\n- **Qwen** - Verified multi-agent collaboration.  \n- **Gemini CLI** - Confirmed working with custom commands.\n- **Codex** - TOML configuration support\n\n### 🔄 ** Likely compatible (MCP client support required)** \n- **Continue.dev** - Has MCP client support\n- **Cursor** - Compatible if/when MQTT/MCP plugin is enabled (check Cursor docs).\n- **Any custom MCP client** - Follow the MCP specification.\n\n### 🧪 **Help Us Test**\nUsing a different AI assistant? We'd love to verify compatibility! Open an issue with your platform details.\n\n**The key is that if your AI assistant supports MCP (Model Context Protocol), it can join the Agent Hub MCP network.**\n\n## Usage\n\n### Complete Workflow Example\n\nHere's a practical example showing frontend and backend agents collaborating on user profile features:\n\n### 1. **Agent Registration**\n```bash\n# In your frontend project (React/Next.js)\n/hub:register\n# Registers as \"frontend\" with capabilities: [\"ui-components\", \"forms\", \"state-management\"]\n\n# In your backend project (Node.js/Express)\n/hub:register  \n# Registers as \"backend\" with capabilities: [\"api-design\", \"database\", \"validation\"]\n```\n\n**Agent Identity & Persistence:**\n- Agents maintain consistent IDs across restarts (no random suffixes)\n- Project path determines agent identity - same path reconnects to existing agent\n- Agent ID conflicts are prevented - can't use existing ID with different project path\n- All messages and context are preserved when agents reconnect\n\n### 2. **User Request & Agent Communication**\n**User (in frontend project)**: \"I need endpoints to create a user profile page and a form to update user information. Can you coordinate with the backend to get the requirements?\"\n\n**Frontend agent**: \n```bash\n# Agent automatically coordinates with backend\n\"Hi backend agent! Working on user profile features. Need:\n- GET endpoint for user profile data\n- PUT endpoint for profile updates  \n- Required fields and validation rules\n- TypeScript types if available\"\n```\n\n### 3. **Backend Response**\n**Backend agent** (after syncing with hub):\n```bash\n# Provides complete API specification\n\"Here's the user profile API design:\n\n## Endpoints\n- GET /api/users/:id - Fetch user profile\n- PUT /api/users/:id - Update user profile\n\n## User Profile Model\n{\n  id: string;\n  email: string;\n  firstName: string;\n  lastName: string;\n  bio?: string;\n  avatar?: string;\n  updatedAt: Date;\n}\n\n## Validation Rules\n- firstName/lastName: 1-50 chars, required\n- bio: max 500 chars, optional\n- avatar: valid URL, optional\n\n## Response Format\nSuccess: { data: UserProfile }\nError: { error: string, field?: string }\n\nReady to implement. Need any specific requirements?\"\n```\n\n### 4. **Frontend Implementation**\n**Frontend agent** (creates plan and executes):\n```bash\n# Agent works autonomously to implement:\n# ✅ Create UserProfile TypeScript interface\n# ✅ Build profile display component\n# ✅ Create profile edit form with validation\n# ✅ Add API service functions\n# ✅ Implement state management\n\n\"✅ User profile features implemented:\n- Profile page with edit/view modes\n- Form validation matching backend rules\n- TypeScript types synced with your API\n- Error handling for all edge cases\"\n```\n\n### Key Features Demonstrated\n\n- **🤝 Cross-Stack Collaboration**: Frontend/backend agents coordinate seamlessly\n- **📋 Complete Specifications**: Detailed API contracts with types and validation\n- **⚡ Autonomous Implementation**: Agents work independently once requirements are clear\n- **💬 Natural Communication**: Agents ask for clarification when needed, otherwise execute plans\n- **✅ End-to-End Features**: Full-stack feature development from API to UI\n\n**Note**: Agents will ask users questions when they need clarification or face ambiguous requirements. Otherwise, they create detailed plans and execute autonomously.\n\n## Core Concepts\n\n### Message Types\n\n- `context` - Share state/configuration\n- `task` - Assign work to agents  \n- `question` - Request information\n- `completion` - Report task completion\n- `error` - Report errors\n\n### Feature Collaboration\n\nStructured multi-agent coordination:\n- Feature-based project organization\n- Task delegation to domain experts\n- Progress tracking through subtasks\n- Context sharing within feature boundaries\n\n## Key MCP Tools\n\nCore tools for multi-agent collaboration:\n- `register_agent` - Register/reconnect an agent\n- `send_message` / `sync` - Inter-agent communication and comprehensive status updates  \n- `get_hub_status` - Hub activity overview\n- `create_feature` / `create_task` - Multi-agent project coordination\n\nSee [System Overview](./docs/SYSTEM-OVERVIEW.md) for complete tool reference and architecture details.\n\n## 🚀 How Multi-Agent Collaboration Works\n\nAgent Hub MCP uses a **feature-based collaboration system** that mirrors real development workflows:\n\n### 1. **Feature Creation**\nCreate multi-agent projects that span different repositories and technologies:\n\n```bash\n# Coordinator agent creates a new feature\ncreate_feature({\n  \"name\": \"user-authentication\", \n  \"title\": \"Add User Authentication System\",\n  \"description\": \"Implement login, signup, and session management across frontend and backend\",\n  \"priority\": \"high\",\n  \"estimatedAgents\": [\"backend-agent\", \"frontend-agent\"]\n})\n```\n\n### 2. **Task Delegation**\nBreak features into specific tasks assigned to domain experts:\n\n```bash\ncreate_task({\n  \"featureId\": \"user-authentication\",\n  \"title\": \"Implement authentication API\",\n  \"delegations\": [\n    { \"agent\": \"backend-agent\", \"scope\": \"Create JWT auth endpoints and middleware\" },\n    { \"agent\": \"frontend-agent\", \"scope\": \"Build login/signup forms and session management\" }\n  ]\n})\n```\n\n### 3. **Intelligent Work Distribution**\nAgents see ALL their work across features and make smart priority decisions:\n\n```bash\n# Backend agent connects and sees:\nsync(\"backend-agent\")\n# Returns:\n{\n  \"workload\": {\n    \"activeFeatures\": [\n      {\n        \"feature\": { \"title\": \"User Authentication\", \"priority\": \"high\" },\n        \"myDelegations\": [{ \"scope\": \"Create JWT auth endpoints\", \"status\": \"pending\" }]\n      },\n      {\n        \"feature\": { \"title\": \"Performance Optimization\", \"priority\": \"critical\" },\n        \"myDelegations\": [{ \"scope\": \"Fix database queries\", \"status\": \"in-progress\" }]\n    }\n  ]\n}\n```\n\n### 4. **Context Sharing & Coordination**\nAgents share implementation details within feature boundaries:\n\n```bash\n# Backend completes API contract\nupdate_subtask({\n  \"featureId\": \"user-authentication\",\n  \"subtaskId\": \"auth-api-contract\", \n  \"status\": \"completed\",\n  \"output\": \"JWT endpoints ready: POST /auth/login, POST /auth/signup, GET /auth/me\"\n})\n\n# Frontend sees progress when checking feature data\nget_feature(\"user-authentication\") \n# Shows: subtask output with JWT endpoints info\n```\n\n### 5. **Automatic Coordination**\nAgents unblock each other by sharing progress and outputs in real-time. The system handles:\n- **Priority management**: Critical tasks get attention first\n- **Dependency tracking**: Agents know what they're waiting for\n- **Context isolation**: Each feature maintains its own scope\n- **Load balancing**: Work distributes naturally across available agents\n\n## Advanced Setup\n\n### Custom Data Directory\n\nTo store Agent Hub MCP data in a custom location, add environment variables to your configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"agent-hub\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"agent-hub-mcp@latest\"],\n      \"env\": {\n        \"AGENT_HUB_DATA_DIR\": \"/path/to/your/data\"\n      }\n    }\n  }\n}\n```\n\n### For Other MCP Clients\n\nIf your AI assistant supports MCP, use these settings:\n- **Command**: `npx -y agent-hub-mcp@latest`  \n- **Protocol**: Standard MCP over stdio  \n- **Data Directory**: `~/.agent-hub` (or set `AGENT_HUB_DATA_DIR`)\n\n## Troubleshooting\n\nCommon issues:\n- MCP server not connecting → Restart AI assistant\n- Commands not recognized → Check custom commands installation\n- Agent ID conflicts → Use unique IDs per project\n\n📖 **Need help?** See [Troubleshooting Guide](./docs/TROUBLESHOOTING.md) for comprehensive solutions.\n\n## Requirements\n\n- Node.js 22+\n- An MCP-compatible AI assistant (Claude Code, Qwen, Gemini, etc.)\n\n## Environment Variables\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `AGENT_HUB_DATA_DIR` | `~/.agent-hub` | Storage directory |\n\n## Contributing\n\nSee [Contributing Guide](./docs/CONTRIBUTING.md) for development setup and guidelines.\n\n## Documentation\n\n- **[System Overview](./docs/SYSTEM-OVERVIEW.md)** - Complete architecture and tool reference\n- **[Troubleshooting](./docs/TROUBLESHOOTING.md)** - Solutions for common issues  \n- **[Known Issues](./docs/KNOWN-ISSUES.md)** - Current limitations and workarounds\n- **[Contributing](./docs/CONTRIBUTING.md)** - Development setup and guidelines\n\n## License\n\nMIT\n\n","readmeFilename":"README.md"}