Chapter 19: Comprehensive Tools Guide - All 47 MCP Tools
This chapter provides a complete reference to all 47 MCP tools Pierre offers for fitness data analysis. You’ll learn tool categories, natural language prompt examples, and how AI assistants discover and use these tools.
Tool Overview
Pierre provides 47 MCP tools organized in 8 functional categories:
┌────────────────────────────────────────────────────────────┐
│ Pierre MCP Tools (47 total) │
├────────────────────────────────────────────────────────────┤
│ 1. Core Fitness Tools (6) │
│ - Activities, athlete profiles, stats │
│ - Provider connection management │
├────────────────────────────────────────────────────────────┤
│ 2. Goals & Planning (4) │
│ - Goal setting, suggestions, feasibility │
│ - Progress tracking │
├────────────────────────────────────────────────────────────┤
│ 3. Performance Analysis (10) │
│ - Activity analysis, metrics calculation │
│ - Performance trends, pattern detection │
│ - Predictions, recommendations │
├────────────────────────────────────────────────────────────┤
│ 4. Configuration Management (6) │
│ - User profiles, training zones │
│ - System configuration catalog │
├────────────────────────────────────────────────────────────┤
│ 5. Fitness Configuration (4) │
│ - Fitness configuration CRUD │
│ - User-specific fitness settings │
├────────────────────────────────────────────────────────────┤
│ 6. Sleep & Recovery (5) │
│ - Sleep quality analysis │
│ - Recovery score calculation │
│ - Rest day suggestions │
├────────────────────────────────────────────────────────────┤
│ 7. Nutrition Tools (5) │
│ - Daily nutrition calculations │
│ - USDA food database search │
│ - Meal analysis │
├────────────────────────────────────────────────────────────┤
│ 8. Recipe Management (7) │
│ - Training-aware meal planning │
│ - Recipe storage and search │
└────────────────────────────────────────────────────────────┘
Tool registry: See src/mcp/schema.rs for the complete tool registration.
For detailed documentation of all 47 tools, see tools-reference.md.
1. Core Fitness Tools (6 Tools)
These tools retrieve fitness data and manage provider connections.
Connect_provider
Description: Connect to a fitness provider (Strava, Fitbit) via unified OAuth flow.
Parameters:
{
"provider": "strava" // Required: "strava" or "fitbit"
}
Natural language prompts:
- “Connect to Strava to get my activities”
- “I want to sync my Fitbit data”
- “Link my Garmin account”
Use case: Initial provider connection or adding additional providers.
Get_connection_status
Description: Check which fitness providers are currently connected.
Parameters: Optional OAuth credentials for custom apps
Natural language prompts:
- “Which providers am I connected to?”
- “Show my connection status”
- “Am I still connected to Strava?”
Use case: Verify active connections before requesting data.
Disconnect_provider
Description: Revoke access tokens for a specific fitness provider.
Parameters:
{
"provider": "strava" // Required
}
Natural language prompts:
- “Disconnect from Strava”
- “Remove my Fitbit connection”
- “Revoke Pierre’s access to my Garmin data”
Use case: Privacy management, switching accounts, troubleshooting.
2. Data Access Tools (4 Tools)
These tools fetch raw data from connected fitness providers.
Get_activities
Description: Retrieve fitness activities from a provider.
Parameters:
{
"provider": "strava", // Required
"limit": 10, // Optional: max activities (default: 10)
"offset": 0 // Optional: pagination offset
}
Natural language prompts:
- “Show me my last 20 Strava runs”
- “Get my recent Fitbit activities”
- “Fetch all my workouts from this month”
Use case: Activity listing, data exploration, trend analysis preparation.
Get_athlete
Description: Get athlete profile from a provider.
Parameters:
{
"provider": "strava" // Required
}
Natural language prompts:
- “Show my Strava profile”
- “What’s my FTP according to Strava?”
- “Get my athlete stats”
Use case: Profile information, baseline metrics (FTP, max HR, weight).
Get_stats
Description: Get aggregate statistics from a provider.
Parameters:
{
"provider": "strava" // Required
}
Natural language prompts:
- “Show my year-to-date running totals”
- “What are my all-time cycling stats?”
- “How much have I run this month?”
Use case: Summary statistics, progress tracking, milestone identification.
Get_activity_intelligence
Description: AI-powered insights and analysis for a specific activity.
Parameters:
{
"activity_id": "12345678", // Required
"provider": "strava", // Required
"include_location": true, // Optional: location intelligence
"include_weather": true // Optional: weather analysis
}
Natural language prompts:
- “Analyze my last run with weather and location insights”
- “What can you tell me about activity 12345678?”
- “Give me intelligent insights on my latest ride”
Use case: Deep activity analysis, performance insights, environmental factors.
Note: OAuth notifications are delivered via Server-Sent Events (SSE) and WebSocket connections rather than as MCP tools. See Chapter 11 (Transport Layers) for details on real-time notification delivery.
3. Intelligence & Analytics Tools (13 Tools)
These tools provide AI-powered analysis and insights.
Analyze_activity
Description: Comprehensive analysis of a single activity.
Natural language prompts:
- “Analyze my activity from yesterday”
- “What insights can you give me about my last ride?”
- “Deep dive into my marathon performance”
Use case: Post-workout analysis, identifying strengths/weaknesses.
Calculate_metrics
Description: Calculate derived metrics from activity data.
Natural language prompts:
- “Calculate my TSS for last week”
- “What’s my Normalized Power for this ride?”
- “Compute training load metrics”
Use case: Advanced metrics not provided by fitness providers.
Analyze_performance_trends
Description: Identify performance trends over time.
Natural language prompts:
- “Am I getting faster at running?”
- “Show my cycling power trends over the last 3 months”
- “Is my fitness improving?”
Use case: Long-term progress tracking, plateau detection.
Compare_activities
Description: Compare two or more activities.
Natural language prompts:
- “Compare my last two 5K runs”
- “How does today’s ride compare to last week?”
- “Show differences between these activities”
Use case: Performance comparison, identifying improvements/regressions.
Detect_patterns
Description: Detect patterns in training data.
Natural language prompts:
- “Find patterns in my running data”
- “Do I always run faster in the morning?”
- “What training patterns lead to my best performances?”
Use case: Optimization insights, habit identification.
Set_goal
Description: Set a fitness goal with target metrics.
Natural language prompts:
- “Set a goal to run a sub-20 minute 5K by June”
- “I want to cycle 200km per week”
- “Target: Complete a marathon in under 4 hours”
Use case: Goal management, motivation tracking.
Track_progress
Description: Track progress towards goals.
Natural language prompts:
- “How am I progressing towards my marathon goal?”
- “Show progress on my weekly cycling target”
- “Am I on track to hit my 5K goal?”
Use case: Goal monitoring, progress visualization.
Suggest_goals
Description: AI-suggested goals based on current fitness level.
Natural language prompts:
- “What goals should I set?”
- “Suggest realistic running goals for me”
- “What’s achievable in the next 3 months?”
Use case: Goal discovery, personalized recommendations.
Analyze_goal_feasibility
Description: Analyze if a goal is realistic given current fitness.
Natural language prompts:
- “Can I realistically run a sub-3 hour marathon?”
- “Is a 100-mile week feasible for me?”
- “Evaluate my goal to bike 50km in under 2 hours”
Use case: Goal validation, expectation management.
Generate_recommendations
Description: Generate training recommendations.
Natural language prompts:
- “What should I work on to improve my cycling?”
- “Give me recommendations for faster 10K times”
- “How can I improve my marathon performance?”
Use case: Training advice, weakness identification.
Calculate_fitness_score
Description: Calculate current fitness score.
Natural language prompts:
- “What’s my current fitness score?”
- “Calculate my fitness level”
- “How fit am I right now?”
Use case: Fitness tracking, periodization planning.
Predict_performance
Description: Predict performance for upcoming events.
Natural language prompts:
- “Predict my marathon time”
- “What pace can I sustain for a half marathon?”
- “Estimate my 5K time based on current fitness”
Use case: Race planning, pacing strategy.
Analyze_training_load
Description: Analyze training stress and recovery needs.
Natural language prompts:
- “Am I overtraining?”
- “What’s my current training load?”
- “Do I need a rest day?”
Use case: Recovery planning, injury prevention.
4. Configuration Management Tools (10 Tools)
These tools manage user profiles and training zones.
Get_configuration_catalog
Description: List all available configuration algorithms and profiles.
Natural language prompts:
- “What configuration profiles are available?”
- “Show me all training zone calculation methods”
Use case: Discovering configuration options.
Get_user_configuration
Description: Retrieve user’s current configuration.
Natural language prompts:
- “Show my current training zones”
- “What’s my configuration?”
Use case: Viewing active settings.
Update_user_configuration
Description: Update user profile (age, weight, FTP, max HR, etc.).
Natural language prompts:
- “Update my FTP to 250 watts”
- “Set my max heart rate to 185”
- “Change my weight to 70kg”
Use case: Profile updates after fitness tests.
Calculate_personalized_zones
Description: Calculate personalized training zones.
Natural language prompts:
- “Calculate my heart rate zones”
- “What are my power zones?”
- “Determine my pace zones”
Use case: Training zone setup.
5. Nutrition Tools (5 Tools)
These tools provide nutrition analysis and planning.
Calculate_daily_nutrition
Description: Calculate daily nutrition needs.
Natural language prompts:
- “How many calories should I eat?”
- “Calculate my daily protein needs”
- “What are my macros?”
Use case: Nutrition planning based on training load.
Search_food
Description: Search USDA food database.
Natural language prompts:
- “Search for ‘banana’ in the food database”
- “Find nutrition info for oatmeal”
Use case: Food logging, meal planning.
Get_food_details
Description: Get detailed nutrition info for a food.
Natural language prompts:
- “Show details for food ID 123456”
- “What nutrients are in this food?”
Use case: Detailed nutrition analysis.
Analyze_meal_nutrition
Description: Analyze complete meal nutrition.
Natural language prompts:
- “Analyze this meal: 100g chicken, 200g rice, 50g broccoli”
- “What’s the nutritional breakdown of my lunch?”
Use case: Meal logging, nutrition tracking.
6. Sleep & Recovery Tools (5 Tools)
These tools analyze sleep and recovery metrics.
Analyze_sleep_quality
Description: Analyze sleep quality and duration.
Natural language prompts:
- “How was my sleep last night?”
- “Analyze my sleep quality”
Use case: Recovery monitoring.
Calculate_recovery_score
Description: Calculate recovery score based on multiple factors.
Natural language prompts:
- “What’s my recovery score?”
- “Am I recovered enough to train hard?”
Use case: Training intensity planning.
Suggest_rest_day
Description: Suggest if a rest day is needed.
Natural language prompts:
- “Do I need a rest day?”
- “Should I take it easy today?”
Use case: Injury prevention, overtraining avoidance.
Tool Chaining Patterns
AI assistants often chain multiple tools together:
Pattern 1: Connect → Fetch → Analyze
User: "Analyze my recent running performance"
AI chains:
1. get_connection_status() // Check if connected
2. get_activities(provider="strava", limit=20) // Fetch runs
3. analyze_performance_trends() // Analyze trends
4. generate_recommendations() // Suggest improvements
Pattern 2: Configuration → Calculation → Recommendation
User: "What should my training zones be?"
AI chains:
1. get_user_configuration() // Get FTP, max HR
2. calculate_personalized_zones() // Calculate zones
3. generate_recommendations() // Training advice for each zone
Pattern 3: Goal Setting → Tracking → Prediction
User: "Set a goal and track my progress"
AI chains:
1. suggest_goals() // Suggest realistic goal
2. set_goal() // Create goal
3. track_progress() // Monitor progress
4. predict_performance() // Estimate completion
Key Takeaways
-
47 total tools: Organized in 8 functional categories for comprehensive fitness analysis.
-
Natural language: AI assistants translate user prompts to tool calls automatically.
-
Tool discovery:
tools/listprovides all tool schemas for AI assistants. -
Connection-first: Most workflows start with connection tools to establish OAuth.
-
Intelligence layer: 10 analytics tools provide AI-powered insights beyond raw data.
-
Configuration-driven: Personalized zones and recommendations based on user profile.
-
Nutrition integration: USDA food database + meal analysis for holistic health.
-
Recovery focus: Sleep and recovery tools prevent overtraining.
-
Recipe management: Training-aware meal planning and recipe storage.
-
Tool chaining: Complex workflows combine multiple tools sequentially.
-
JSON Schema: Every tool has input schema for validation and type safety.
See tools-reference.md for complete tool documentation.
Next Chapter: Chapter 20: Sports Science Algorithms & Intelligence - Learn how Pierre implements sports science algorithms for TSS, CTL/ATL/TSB, VO2 max estimation, FTP detection, and performance predictions.