# Phase 4: Production Enhancements & Advanced Features
**Status**: 🟡 Planning
**Start Date**: 2025-11-16
**Estimated Duration**: 8-12 hours
---
## Overview
Phase 4 builds on the complete MCP server implementation (Phases 1-3) with production enhancements, advanced features, usage examples, and performance optimizations.
### Goals
1. **Complete Missing Features**: Implement deferred functions
2. **Usage Examples**: Practical examples for common workflows
3. **Performance Monitoring**: Metrics, profiling, and optimization
4. **Advanced Features**: Real-time streaming, multi-robot support
5. **Production Ready**: Deployment guides, CI/CD, monitoring
---
## Phase 4 Modules
### Module 4.1: Missing Implementations (3-4 hours)
#### 1. Complete set_model_state() ⏳
**File**: `src/gazebo_mcp/tools/model_management.py`
**Priority**: High
**Scope**:
```python
def set_model_state(
model_name: str,
pose: Optional[Dict] = None,
twist: Optional[Dict] = None,
reference_frame: str = "world"
) -> OperationResult:
"""
Set model pose and/or velocity.
Args:
model_name: Name of model to update
pose: Target pose {position: {x,y,z}, orientation: {roll,pitch,yaw}}
twist: Target velocity {linear: {x,y,z}, angular: {x,y,z}}
reference_frame: Reference frame for pose
Returns:
OperationResult with update status
"""
# Validate inputs
model_name = validate_model_name(model_name)
# Get bridge
bridge = _get_bridge()
# Convert pose/twist to ROS2 format
if pose:
ros_pose = dict_to_pose(pose)
if twist:
ros_twist = dict_to_twist(twist)
# Call /gazebo/set_entity_state service
success = bridge.set_entity_state(
model_name,
pose=ros_pose,
twist=ros_twist,
reference_frame=reference_frame
)
return success_result({"model": model_name, "updated": True})
```
**Tasks**:
- [ ] Implement `GazeboBridgeNode.set_entity_state()` service call
- [ ] Add pose/twist validation and conversion
- [ ] Write tests for set_model_state()
- [ ] Update MCP adapter with set_model_state tool
- [ ] Document usage examples
**Estimated Time**: 2 hours
---
#### 2. Implement Real World Property Setting ⏳
**File**: `src/gazebo_mcp/tools/world_tools.py`
**Priority**: Medium
**Scope**:
- Implement actual `set_world_property()` via Gazebo services
- Support gravity, physics_update_rate, max_step_size
- Add validation for all property types
**Tasks**:
- [ ] Research Gazebo services for property setting
- [ ] Implement property setters in GazeboBridgeNode
- [ ] Add validation for each property type
- [ ] Test with real Gazebo
- [ ] Update documentation
**Estimated Time**: 1-2 hours
---
### Module 4.2: Usage Examples (2-3 hours)
#### 3. Create Practical Examples ⏳
**Directory**: `examples/`
**Priority**: High
**Examples to Create**:
**`examples/01_basic_connection.py`**
```python
"""
Basic connection and model listing.
Demonstrates:
- Connecting to Gazebo via MCP
- Listing all models
- Querying simulation status
"""
from mcp.server.server import GazeboMCPServer
def main():
# Create MCP server
server = GazeboMCPServer()
# List all tools
tools = server.list_tools()
print(f"Available tools: {len(tools)}")
# Get simulation status
result = server.call_tool("gazebo_get_simulation_status", {})
print(result)
# List models (summary)
result = server.call_tool("gazebo_list_models", {
"response_format": "summary"
})
print(result)
if __name__ == "__main__":
main()
```
**`examples/02_spawn_and_control.py`**
```python
"""
Spawn a model and control it.
Demonstrates:
- Spawning a simple box model
- Querying model state
- Setting model position
- Deleting model
"""
```
**`examples/03_sensor_streaming.py`**
```python
"""
Stream sensor data.
Demonstrates:
- Listing available sensors
- Subscribing to sensor streams
- Reading sensor data
- Processing camera/lidar data
"""
```
**`examples/04_simulation_control.py`**
```python
"""
Control simulation state.
Demonstrates:
- Pausing/unpausing simulation
- Resetting simulation
- Querying simulation time
- Getting world properties
"""
```
**`examples/05_complete_workflow.py`**
```python
"""
Complete robot testing workflow.
Demonstrates:
1. Start with empty world
2. Spawn robot at position
3. List sensors on robot
4. Run simulation for 10 seconds
5. Query sensor data
6. Move robot to new position
7. Verify movement
8. Clean up
"""
```
**Tasks**:
- [ ] Create `examples/` directory
- [ ] Implement all 5 example scripts
- [ ] Add detailed comments and docstrings
- [ ] Create `examples/README.md` with usage guide
- [ ] Test all examples with real Gazebo
- [ ] Add examples to main README
**Estimated Time**: 2-3 hours
---
### Module 4.3: Performance Monitoring (2-3 hours)
#### 4. Add Metrics and Profiling ⏳
**Files**: `src/gazebo_mcp/utils/metrics.py`, `src/gazebo_mcp/utils/profiler.py`
**Priority**: Medium
**Scope**:
**Metrics Collection**:
```python
class MetricsCollector:
"""Collect performance metrics."""
def __init__(self):
self.metrics = {
"tool_calls": {}, # Count per tool
"response_times": {}, # Average per tool
"errors": {}, # Count per error type
"tokens_saved": 0, # Total via ResultFilter
}
def record_tool_call(self, tool_name: str, duration: float,
tokens_sent: int, tokens_saved: int):
"""Record tool execution metrics."""
def get_summary(self) -> Dict:
"""Get metrics summary."""
def export_prometheus(self) -> str:
"""Export in Prometheus format."""
```
**Profiling**:
```python
@profile_tool
def list_models(...):
"""Automatically profile execution."""
# Generates:
# - Execution time
# - Memory usage
# - ROS2 call count
# - Token efficiency
```
**Tasks**:
- [ ] Implement MetricsCollector class
- [ ] Add metrics to all tools
- [ ] Create profiling decorator
- [ ] Add `/metrics` endpoint (optional)
- [ ] Create metrics dashboard script
- [ ] Document metrics in README
**Estimated Time**: 2-3 hours
---
### Module 4.4: Advanced Features (3-4 hours)
#### 5. Real-time Sensor Streaming ⏳
**File**: `src/gazebo_mcp/tools/sensor_tools.py`
**Priority**: Medium
**Enhancements**:
```python
def stream_sensor_data(
sensor_name: str,
topic_name: str,
callback: Callable[[Dict], None],
buffer_size: int = 10,
rate_limit: Optional[float] = None
) -> OperationResult:
"""
Stream sensor data with callbacks.
Args:
sensor_name: Sensor identifier
topic_name: ROS2 topic
callback: Function to call with each message
buffer_size: Number of messages to buffer
rate_limit: Max messages per second
"""
# Create buffered subscription
# Apply rate limiting
# Call callback on each message
```
**Tasks**:
- [ ] Implement buffered subscriptions
- [ ] Add rate limiting
- [ ] Support multiple simultaneous streams
- [ ] Add stream management (pause/resume/stop)
- [ ] Test with high-frequency sensors (lidar)
**Estimated Time**: 2 hours
---
#### 6. Multi-Robot Support ⏳
**File**: `src/gazebo_mcp/tools/multi_robot.py`
**Priority**: Low
**Scope**:
```python
def spawn_robot_fleet(
robot_type: str,
count: int,
formation: str = "grid",
spacing: float = 2.0,
namespace_prefix: str = "robot"
) -> OperationResult:
"""
Spawn multiple robots in formation.
Args:
robot_type: Type of robot to spawn
count: Number of robots
formation: "grid", "circle", "line"
spacing: Distance between robots
namespace_prefix: ROS2 namespace prefix
"""
# Calculate positions based on formation
# Spawn each robot with unique name/namespace
# Return fleet info
def coordinate_robots(
robot_names: List[str],
command: str, # "move_to", "follow_leader", "formation"
parameters: Dict
) -> OperationResult:
"""Coordinate multiple robots."""
```
**Tasks**:
- [ ] Implement formation calculations
- [ ] Add namespace management
- [ ] Create coordination commands
- [ ] Test with multiple TurtleBot3s
- [ ] Document multi-robot workflows
**Estimated Time**: 2 hours
---
### Module 4.5: Production Deployment (2-3 hours)
#### 7. Deployment Guides ⏳
**Files**: `docs/DEPLOYMENT.md`, `docker/`, `.github/workflows/`
**Priority**: Medium
**Deliverables**:
**Docker Support**:
```dockerfile
# Dockerfile
FROM ros:humble-ros-base
# Install Gazebo
RUN apt-get update && apt-get install -y \
ros-humble-gazebo-ros-pkgs \
python3-pip
# Copy project
COPY . /workspace/ros2_gazebo_mcp
WORKDIR /workspace/ros2_gazebo_mcp
# Install dependencies
RUN pip install -r requirements.txt
# Source ROS2
RUN echo "source /opt/ros/humble/setup.bash" >> ~/.bashrc
# Expose MCP server
CMD ["python", "-m", "mcp.server.server"]
```
**Docker Compose**:
```yaml
# docker-compose.yml
version: '3.8'
services:
gazebo:
image: gazebo:harmonic
environment:
- DISPLAY=$DISPLAY
volumes:
- /tmp/.X11-unix:/tmp/.X11-unix
mcp_server:
build: .
depends_on:
- gazebo
environment:
- ROS_DOMAIN_ID=0
```
**CI/CD Pipeline**:
```yaml
# .github/workflows/test.yml
name: Test
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v3
- name: Setup ROS2
uses: ros-tooling/setup-ros@v0.6
with:
required-ros-distributions: humble
- name: Install dependencies
run: |
pip install -r requirements.txt
pip install -r requirements-dev.txt
- name: Run unit tests
run: pytest tests/test_utils.py -v
- name: Run integration tests
run: |
source /opt/ros/humble/setup.bash
pytest tests/test_integration.py -v --with-ros2
```
**Tasks**:
- [ ] Create Dockerfile and docker-compose.yml
- [ ] Write DEPLOYMENT.md guide
- [ ] Set up GitHub Actions CI/CD
- [ ] Add systemd service file
- [ ] Document production best practices
**Estimated Time**: 2-3 hours
---
#### 8. Monitoring & Observability ⏳
**Files**: `src/gazebo_mcp/monitoring/`, `docs/MONITORING.md`
**Priority**: Low
**Scope**:
- Health check endpoint
- Prometheus metrics export
- Structured logging (JSON)
- Error tracking integration
- Performance dashboards
**Tasks**:
- [ ] Implement health check
- [ ] Add Prometheus exporter
- [ ] Configure structured logging
- [ ] Create Grafana dashboard template
- [ ] Document monitoring setup
**Estimated Time**: 2 hours
---
## Phase 4 Summary
### Priorities
**Must Have (High Priority)**:
1. ✅ Complete `set_model_state()` implementation
2. ✅ Create 5 usage examples
3. ✅ Add performance metrics
**Should Have (Medium Priority)**:
4. ⚪ Implement real world property setting
5. ⚪ Real-time sensor streaming enhancements
6. ⚪ Docker deployment support
7. ⚪ CI/CD pipeline
**Nice to Have (Low Priority)**:
8. ⚪ Multi-robot coordination
9. ⚪ Monitoring dashboards
10. ⚪ Advanced profiling
### Estimated Timeline
| Module | Tasks | Time | Priority |
|--------|-------|------|----------|
| 4.1: Missing Implementations | 2 tasks | 3-4 hours | High |
| 4.2: Usage Examples | 5 examples | 2-3 hours | High |
| 4.3: Performance Monitoring | Metrics + profiling | 2-3 hours | Medium |
| 4.4: Advanced Features | Streaming + multi-robot | 3-4 hours | Medium |
| 4.5: Production Deployment | Docker + CI/CD + monitoring | 4-5 hours | Medium |
| **TOTAL** | **~15 tasks** | **14-19 hours** | - |
### Success Criteria
Phase 4 is complete when:
- [x] All missing core functions implemented
- [x] 5+ working usage examples available
- [x] Performance metrics collection working
- [x] Docker deployment tested
- [x] CI/CD pipeline running
- [x] Documentation updated
---
## Getting Started with Phase 4
### Quick Start Options
**Option A: Complete Core Features First** (Recommended)
```bash
# Focus on missing implementations and examples
1. Implement set_model_state()
2. Create usage examples
3. Test thoroughly
```
**Option B: Production Ready**
```bash
# Focus on deployment and monitoring
1. Create Docker setup
2. Add CI/CD pipeline
3. Implement metrics
4. Add monitoring
```
**Option C: Advanced Features**
```bash
# Focus on new capabilities
1. Real-time streaming
2. Multi-robot support
3. Performance optimizations
```
### Next Steps
To start Phase 4, choose a starting point:
1. **Start with Module 4.1** - Complete missing implementations
2. **Start with Module 4.2** - Create usage examples
3. **Start with Module 4.5** - Production deployment setup
Which module would you like to start with?
---
**Document Status**: 🟡 Planning Phase
**Last Updated**: 2025-11-16
**Ready to Begin**: ✅ Yes - All prerequisites complete
