Update Claude Task Master AI integration status with comprehensive documentation

- Added detailed status report covering completed work
- Documented current configuration for Ollama integration
- Listed all available MCP tools and their functionality
- Included troubleshooting guide and next steps
- Documented architecture and workflow for VS Code MCP integration

research/claude-task-master-ai-integration-status.md

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+# Claude Task Master AI - Nix Package & VS Code MCP Integration
+
+## Current Status
+
+### ✅ Completed
+1. **Nix Package**: Successfully built and packaged Claude Task Master AI
+2. **Local Installation**: Binary available at `/home/geir/Home-lab/result/bin/task-master-ai`
+3. **Ollama Integration**: Configured to use local Ollama models on grey-area:11434
+4. **VS Code MCP Setup**: Configured for integration with Cursor/VS Code
+
+### 🔧 Configuration Files
+
+#### Task Master Configuration
+- **Location**: `/home/geir/Home-lab/.taskmaster/config.json`
+- **Models**: 
+  - Main: `qwen3:4b` (general tasks)
+  - Research: `deepseek-r1:1.5b` (reasoning tasks)
+  - Fallback: `gemma3:4b-it-qat` (backup)
+- **Provider**: `openai` (using OpenAI-compatible API)
+- **Base URL**: `http://grey-area:11434/v1`
+
+#### VS Code MCP Configuration
+- **Location**: `/home/geir/Home-lab/.cursor/mcp.json`
+- **Command**: Direct path to Nix-built binary
+- **Environment**: OpenAI-compatible mode with local Ollama
+
+### 🎯 Available MCP Tools
+
+The Task Master MCP server provides these tools for AI assistants:
+
+#### Project Management
+- `initialize_project` - Set up new Task Master project
+- `models` - Configure AI models and check status
+- `parse_prd` - Generate tasks from PRD documents
+
+#### Task Operations
+- `get_tasks` - List all tasks with filtering
+- `get_task` - Get specific task details
+- `next_task` - Find next task to work on
+- `add_task` - Create new tasks with AI
+- `update_task` - Update existing task
+- `set_task_status` - Change task status
+- `remove_task` - Delete tasks
+
+#### Subtask Management
+- `add_subtask` - Add subtasks to existing tasks
+- `update_subtask` - Update subtask information
+- `remove_subtask` - Remove subtasks
+- `clear_subtasks` - Clear all subtasks from tasks
+
+#### Advanced Features
+- `expand_task` - Break down tasks into subtasks
+- `expand_all` - Auto-expand all pending tasks
+- `analyze_project_complexity` - Complexity analysis
+- `complexity_report` - View complexity reports
+
+#### Dependencies
+- `add_dependency` - Create task dependencies
+- `remove_dependency` - Remove dependencies
+- `validate_dependencies` - Check for issues
+- `fix_dependencies` - Auto-fix dependency problems
+
+### 🚀 Usage in VS Code
+
+1. **Restart VS Code/Cursor** after updating `.cursor/mcp.json`
+2. **Access via AI Chat**: Use Claude or GitHub Copilot
+3. **Example Commands**:
+   - "Initialize a new Task Master project in my current directory"
+   - "Create a task for setting up a new home lab service"
+   - "Show me the next task I should work on"
+   - "Expand task 5 into detailed subtasks"
+
+### 🔍 Current Issue
+
+The Task Master binary works as an MCP server but appears to hang when making AI API calls to Ollama. This might be due to:
+
+1. **Network connectivity** between the host and grey-area
+2. **OpenAI API compatibility** formatting differences
+3. **Authentication** handling with the fake API key
+
+**Workaround**: Use Task Master through the MCP interface in VS Code, where the AI assistant can handle the tool calls without direct API communication.
+
+### 🛠️ Troubleshooting
+
+#### Check Ollama Connectivity
+```bash
+curl -X POST http://grey-area:11434/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "qwen3:4b", "messages": [{"role": "user", "content": "Hello"}]}'
+```
+
+#### Verify Task Master Tools
+```bash
+cd /home/geir/Home-lab
+echo '{"jsonrpc": "2.0", "id": 1, "method": "tools/list", "params": {}}' | \
+  OPENAI_API_KEY="fake-key" OPENAI_BASE_URL="http://grey-area:11434/v1" \
+  ./result/bin/task-master-ai
+```
+
+#### Check MCP Server Status
+In VS Code, open the Output panel and look for MCP server connection logs.
+
+### 📋 Next Steps
+
+1. **Test MCP Integration**: Try using Task Master tools through VS Code AI chat
+2. **Debug API Connectivity**: Investigate why Task Master hangs on API calls
+3. **Create Sample Project**: Initialize a test project to validate functionality
+4. **Documentation**: Create user guides for common workflows
+
+### 🏗️ Architecture
+
+```
+VS Code/Cursor (AI Chat)
+    ↓ MCP Protocol
+Task Master AI (Nix Binary)
+    ↓ OpenAI-compatible API
+Ollama (grey-area:11434)
+    ↓ Model Inference
+Local Models (qwen3:4b, deepseek-r1:1.5b, gemma3:4b-it-qat)
+```
+
+This setup provides a complete local AI-powered task management system integrated with your development environment while maintaining full privacy and control over your data.