# Phase 4 MCP Tools Implementation - Complete
## Summary
Successfully implemented 6 Phase 4 MCP tools for Session-Buddy, adding advanced analytics capabilities to the skills tracking system.
## Files Created
### 1. `/Users/les/Projects/session-buddy/session_buddy/mcp/tools/skills/phase4_tools.py`
**Main implementation file** containing all 6 Phase 4 tools:
- `get_real_time_metrics` - Real-time dashboard metrics
- `detect_anomalies` - Performance anomaly detection via Z-score
- `get_skill_trend` - Trend analysis using linear regression
- `get_collaborative_recommendations` - Collaborative filtering recommendations
- `get_community_baselines` - Global skill effectiveness baselines
- `get_skill_dependencies` - Co-occurrence analysis with lift scores
### 2. `/Users/les/Projects/session-buddy/session_buddy/mcp/tools/skills/__init__.py`
**Package initialization** for the skills tools module.
### 3. Updated `/Users/les/Projects/session-buddy/session_buddy/mcp/tools/__init__.py`
**Added import** for `register_phase4_tools` to make it available to the MCP server.
### 4. Updated `/Users/les/Projects/session-buddy/session_buddy/mcp/server.py`
**Registered the tools** with the MCP server by:
- Importing `register_phase4_tools`
- Calling it during server initialization
## Tool Specifications
### 1. get_real_time_metrics
**Purpose**: Get real-time skill metrics for dashboard display
**Input Schema**:
```python
{
"type": "object",
"properties": {
"limit": {"type": "integer", "default": 10},
"time_window_hours": {"type": "number", "default": 1.0}
}
}
```
**Returns**:
```python
{
"success": True,
"top_skills": [
{
"skill_name": "pytest-run",
"invocation_count": 42,
"completed_count": 38,
"avg_duration": 45.2,
"last_invocation_at": "2026-02-10T12:00:00Z"
}
],
"timestamp": "2026-02-10T12:00:00Z",
"message": "Found 5 skills in the last 1.0 hours"
}
```
**Use Cases**:
- Dashboard widgets showing recent skill activity
- Real-time monitoring of skill usage
- Identification of currently popular skills
### 2. detect_anomalies
**Purpose**: Detect performance anomalies using Z-score analysis
**Input Schema**:
```python
{
"type": "object",
"properties": {
"threshold": {"type": "number", "default": 2.0},
"time_window_hours": {"type": "number", "default": 24.0}
}
}
```
**Returns**:
```python
{
"success": True,
"anomalies": [
{
"skill_name": "ruff-check",
"anomaly_type": "performance_drop",
"baseline_value": 0.92,
"observed_value": 0.65,
"deviation_score": -2.7
}
],
"timestamp": "2026-02-10T12:00:00Z",
"message": "Detected 1 anomaly(ies) with Z-score >= 2.0"
}
```
**Use Cases**:
- Alert on sudden performance degradation
- Detect broken skills (high failure rates)
- Identify unusual usage patterns
### 3. get_skill_trend
**Purpose**: Analyze skill effectiveness trend over time
**Input Schema**:
```python
{
"type": "object",
"properties": {
"skill_name": {"type": "string"},
"days": {"type": "integer", "default": 7}
}
}
```
**Returns**:
```python
{
"success": True,
"skill_name": "pytest-run",
"trend": "improving",
"slope": 0.0123,
"start_value": 0.75,
"end_value": 0.82,
"change_percent": 9.3,
"confidence": 0.04,
"timestamp": "2026-02-10T12:00:00Z"
}
```
**Use Cases**:
- Track skill improvement over time
- Identify degrading skills
- Measure impact of optimizations
### 4. get_collaborative_recommendations
**Purpose**: Get personalized skill recommendations using collaborative filtering
**Input Schema**:
```python
{
"type": "object",
"properties": {
"user_id": {"type": "string"},
"limit": {"type": "integer", "default": 5}
}
}
```
**Returns**:
```python
{
"success": True,
"user_id": "user-123",
"recommendations": [
{
"skill_name": "coverage-report",
"score": 0.87,
"completion_rate": 0.91,
"source": "collaborative_filtering"
}
],
"timestamp": "2026-02-10T12:00:00Z",
"message": "Generated 5 recommendations for user-123"
}
```
**Use Cases**:
- Suggest skills based on similar users
- Cold start problem resolution
- Personalized skill discovery
### 5. get_community_baselines
**Purpose**: Get global skill effectiveness baselines
**Input Schema**:
```python
{
"type": "object",
"properties": {
"limit": {"type": "integer", "default": 20}
}
}
```
**Returns**:
```python
{
"success": True,
"baselines": [
{
"skill_name": "pytest-run",
"total_users": 15,
"total_invocations": 342,
"global_completion_rate": 0.88,
"effectiveness_percentile": 75.3
}
],
"timestamp": "2026-02-10T12:00:00Z",
"message": "Retrieved 20 community baselines"
}
```
**Use Cases**:
- Compare personal performance vs. community
- Identify globally effective skills
- Benchmarking and rankings
### 6. get_skill_dependencies
**Purpose**: Get skills commonly used together with a given skill
**Input Schema**:
```python
{
"type": "object",
"properties": {
"skill_name": {"type": "string"},
"limit": {"type": "integer", "default": 10},
"min_lift": {"type": "number", "default": 1.5}
}
}
```
**Returns**:
```python
{
"success": True,
"skill_name": "pytest-run",
"dependencies": [
{
"skill_b": "ruff-check",
"co_occurrence_count": 45,
"lift_score": 2.3,
"relationship_type": "strong_positive"
}
],
"timestamp": "2026-02-10T12:00:00Z",
"message": "Found 5 skills with lift >= 1.5"
}
```
**Use Cases**:
- Workflow optimization
- Skill bundling recommendations
- Understanding usage patterns
## Integration Points
The Phase 4 tools integrate with existing Session-Buddy infrastructure:
### Storage Layer
- **SkillsStorage** (`session_buddy/storage/skills_storage.py`)
- `get_real_time_metrics()` - Line 1386
- `detect_anomalies()` - Line 1454
- `aggregate_hourly_metrics()` - Line 1569
- `get_community_baselines()` - Line 1660
- `get_similar_users()` - Line 1711
- `update_skill_dependencies()` - Line 1814
### Analytics Layer
- **TimeSeriesAnalyzer** (`session_buddy/analytics/time_series.py`)
- `detect_trend()` - Line 158
- `aggregate_hourly_metrics()` - Line 89
- **CollaborativeFilteringEngine** (`session_buddy/analytics/collaborative_filtering.py`)
- `recommend_from_similar_users()` - Line 248
- `get_global_fallback_recommendations()` - Line 496
- `update_community_baselines()` - Line 387
## Architecture Compliance
The implementation follows Session-Buddy's MCP tool patterns:
1. **Async Functions**: All tools are async for proper MCP integration
2. **Error Handling**: Try-catch blocks with graceful error returns
3. **JSON Responses**: All return values are JSON-serializable
4. **Timestamps**: ISO format strings (not datetime objects)
5. **Consistent Structure**: All responses include `success`, `message`, and `timestamp` fields
6. **Documentation**: Comprehensive docstrings with Args/Returns/Examples
## Testing
### Registration Test
Run `/Users/les/Projects/session-buddy/PHASE4_MCP_TOOLS_SUMMARY.md` to verify:
- All 6 tool functions exist and are callable
- Registration function exists
- All tools have proper documentation
### Output
```
============================================================
✅ All Phase 4 MCP Tools Registration Tests Passed!
============================================================
Phase 4 MCP Tools Summary:
------------------------------------------------------------
1. get_real_time_metrics - Dashboard metrics for recent skills
2. detect_anomalies - Performance anomaly detection
3. get_skill_trend - Trend analysis over time
4. get_collaborative_recommendations - Personalized recommendations
5. get_community_baselines - Global skill effectiveness
6. get_skill_dependencies - Co-occurrence analysis
------------------------------------------------------------
```
## Usage Example
```python
# Import tools
from session_buddy.mcp.tools.skills.phase4_tools import (
get_real_time_metrics,
detect_anomalies,
)
# Get recent metrics
result = await get_real_time_metrics(limit=5, time_window_hours=1.0)
print(f"Top skills: {[s['skill_name'] for s in result['top_skills']]}")
# Detect anomalies
result = await detect_anomalies(threshold=2.0, time_window_hours=24.0)
print(f"Anomalies found: {len(result['anomalies'])}")
```
## Server Registration
The tools are automatically registered when the MCP server starts:
```python
# In /Users/les/Projects/session-buddy/session_buddy/mcp/server.py
from .tools import register_phase4_tools
register_phase4_tools(mcp) # Registers all 6 tools
```
## Dependencies
The Phase 4 tools depend on:
1. **session_buddy.storage.skills_storage** - Data access layer
2. **session_buddy.analytics.time_series** - Trend analysis
3. **session_buddy.analytics.collaborative_filtering** - Recommendations
All dependencies are lazy-imported within the tool functions to avoid circular imports.
## Error Handling
All tools implement consistent error handling:
```python
try:
# Tool implementation
result = storage.get_real_time_metrics(...)
return {"success": True, "top_skills": result, ...}
except Exception as e:
logger.error(f"Failed to get real-time metrics: {e}")
return {
"success": False,
"top_skills": [],
"message": f"Error: {str(e)}"
}
```
## Next Steps
1. **Database Initialization**: The tools require the skills database schema to be initialized
2. **Sample Data**: Populate with test data to verify functionality
3. **Integration Testing**: Test tools through the actual MCP server interface
4. **Performance Monitoring**: Benchmark tool response times
## Files Modified
- `/Users/les/Projects/session-buddy/session_buddy/mcp/tools/__init__.py` - Added import
- `/Users/les/Projects/session-buddy/session_buddy/mcp/server.py` - Registered tools
## Files Created
- `/Users/les/Projects/session-buddy/session_buddy/mcp/tools/skills/phase4_tools.py` - Main implementation
- `/Users/les/Projects/session-buddy/session_buddy/mcp/tools/skills/__init__.py` - Package init
- `/Users/les/Projects/session-buddy/PHASE4_MCP_TOOLS_SUMMARY.md` - Test script
- `/Users/les/Projects/session-buddy/test_phase4_mcp_tools.py` - Simple test
- `/Users/les/Projects/session-buddy/test_phase4_mcp_with_db.py` - Full test with DB
## Conclusion
All 6 Phase 4 MCP tools have been successfully implemented and integrated into the Session-Buddy MCP server. The tools provide comprehensive analytics capabilities including real-time monitoring, anomaly detection, trend analysis, collaborative filtering, community baselines, and dependency analysis.
