# Pierre Examples - MCP & A2A Protocols
Comprehensive examples demonstrating both **MCP (Model Context Protocol)** for AI assistants and **A2A (Agent-to-Agent)** protocol for autonomous agent communication.
## š¤ MCP Client Examples
**MCP** is for interactive AI assistants (Claude, ChatGPT, custom LLMs) to query fitness data in real-time.
### Gemini Fitness Assistant (`mcp_clients/gemini_fitness_assistant/`)
An interactive AI fitness assistant using **Google's free Gemini API** with Pierre MCP Server:
- **Free LLM Integration**: Uses Gemini API (1,500 requests/day, no credit card)
- **MCP Protocol**: Direct HTTP JSON-RPC communication with Pierre
- **Function Calling**: Native tool calling for fitness data analysis
- **End-to-End Example**: Complete open-source AI assistant alternative
```bash
# Run the Gemini fitness assistant
cd mcp_clients/gemini_fitness_assistant
pip install -r requirements.txt
export GEMINI_API_KEY='your-api-key'
export PIERRE_EMAIL='user@example.com'
export PIERRE_PASSWORD='password'
python gemini_fitness_assistant.py
```
Get a free Gemini API key at: https://ai.google.dev/gemini-api/docs/api-key
**What it demonstrates**: How any free LLM service with function calling can interact with Pierre MCP Server to build an AI fitness assistant without proprietary solutions like Claude Desktop.
---
## š A2A Agent Examples
**A2A** is for autonomous agents communicating and delegating tasks without human intervention.
### What is A2A?
The **Agent-to-Agent (A2A) protocol** is an open standard for AI agent communication, developed by Google and housed by the Linux Foundation. A2A enables autonomous agents to:
- **Discover Capabilities**: Find and evaluate other agents via agent cards
- **Delegate Tasks**: Request work from other agents without understanding their internals
- **Monitor Progress**: Track long-running tasks with status updates
- **Collaborate**: Multiple agents working together on complex problems
- **Operate Autonomously**: Agents run independently, making decisions without human intervention
### Available A2A Examples
#### 1. **Agent Discovery** (`agents/agent_discovery/`)
**What it demonstrates**: Agent card discovery and capability negotiation
Learn how agents discover each other's capabilities before collaboration:
- Fetch agent cards to see available tools and features
- Parse and validate agent capabilities
- Negotiate authentication methods (OAuth2, API Key)
- Make informed decisions about which agent to use
```bash
cd agents/agent_discovery
cargo run
```
**Key Concepts**: Agent cards, capability matching, authentication selection
---
#### 2. **Task Lifecycle Management** (`agents/task_manager/`)
**What it demonstrates**: Long-running task management and status tracking
See how A2A handles asynchronous operations:
- Create tasks for long-running analysis
- Poll task status (pending ā running ā completed/failed)
- Retrieve task results when complete
- List and filter tasks by status
- Handle task failures gracefully
```bash
cd agents/task_manager
export PIERRE_A2A_CLIENT_ID="your_client_id"
export PIERRE_A2A_CLIENT_SECRET="your_client_secret"
cargo run
```
**Key Concepts**: Task lifecycle, status polling, asynchronous execution
---
#### 3. **Fitness Analysis Agent** (`agents/fitness_analyzer/`)
**What it demonstrates**: Production-ready autonomous agent
A complete autonomous agent that:
- Runs continuously without human intervention
- Authenticates via A2A client credentials
- Fetches fitness data from connected providers
- Performs intelligent pattern analysis
- Generates JSON reports with insights
- Handles errors and retries gracefully
```bash
cd agents/fitness_analyzer
./run.sh --setup-demo --dev
```
**Key Concepts**: Autonomous operation, data analysis, production deployment
---
## Protocol Comparison: MCP vs A2A
| Feature | MCP | A2A |
|---------|-----|-----|
| **Communication** | HTTP JSON-RPC / SSE | HTTP REST |
| **Session Model** | Stateful | Stateless |
| **Latency** | Ultra-low (ms) | Standard (100s ms) |
| **Throughput** | Medium | High |
| **Use Case** | Interactive AI Assistants | Autonomous Agents |
| **Example** | Gemini Fitness Assistant | Fitness Analyzer |
| **Auth** | JWT (user context) | Client credentials |
| **Context** | Rich session context | Request/response only |
| **Discovery** | Resources/Tools | Agent cards |
| **Human Interaction** | Yes (conversational) | No (automated) |
| **Best For** | AI assistants, IDEs | Agent-to-agent, automation |
### When to Use Each Protocol
| Scenario | Protocol | Reason |
|----------|----------|--------|
| AI assistant (Claude, ChatGPT) asking questions | **MCP** | Interactive, low-latency, human-in-loop |
| Custom LLM querying fitness data | **MCP** | Real-time tool calling, stateful session |
| Scheduled fitness report generation | **A2A** | Autonomous, no human needed |
| Real-time data exploration | **MCP** | Stateful session, context preservation |
| Multi-agent collaboration | **A2A** | Agents delegating work to each other |
| Batch processing 1000s of records | **A2A** | High throughput, async tasks |
| Interactive debugging | **MCP** | Rich tooling, IDE integration |
**Rule of Thumb**:
- Use **MCP** when a human or AI assistant is actively involved
- Use **A2A** when agents work autonomously or delegate to other agents
---
## Quick Start Guide
### Prerequisites
1. **Start Pierre Server**:
```bash
cd pierre_mcp_server
cargo run --bin pierre-mcp-server
```
2. **Create a User Account**:
```bash
curl -X POST http://localhost:8081/admin/setup \
-H "Content-Type: application/json" \
-d '{
"email": "user@example.com",
"password": "SecurePass123!",
"display_name": "Test User"
}'
```
### Choose Your Path
#### Path A: MCP Client (Interactive AI Assistant)
```bash
# Setup Gemini Fitness Assistant
cd mcp_clients/gemini_fitness_assistant
pip install -r requirements.txt
# Get free Gemini API key: https://ai.google.dev/gemini-api/docs/api-key
export GEMINI_API_KEY='your-api-key'
export PIERRE_EMAIL='user@example.com'
export PIERRE_PASSWORD='SecurePass123!'
# Run interactive assistant
python gemini_fitness_assistant.py
```
#### Path B: A2A Agent (Autonomous Operation)
```bash
# Register A2A client (one-time setup)
ADMIN_TOKEN=$(curl -s -X POST http://localhost:8081/admin/setup \
-H "Content-Type: application/json" \
-d '{"email": "admin@example.com", "password": "SecurePass123!", "display_name": "Admin"}' | \
jq -r '.admin_token')
# Register A2A client
CREDENTIALS=$(curl -s -X POST http://localhost:8081/a2a/clients \
-H "Authorization: Bearer $ADMIN_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"name": "Demo Agent",
"description": "A2A demo client",
"capabilities": ["fitness-analysis"],
"contact_email": "demo@example.com"
}')
echo $CREDENTIALS | jq '.'
# Save client_id and client_secret for examples
# Run examples
cd agents/agent_discovery
cargo run
# Or run task manager
cd agents/task_manager
export PIERRE_A2A_CLIENT_ID="your_client_id"
export PIERRE_A2A_CLIENT_SECRET="your_client_secret"
cargo run
# Or run fitness analyzer
cd agents/fitness_analyzer
./run.sh --setup-demo --dev
```
---
## Directory Structure
```
examples/
āāā mcp_clients/
ā āāā gemini_fitness_assistant/ # Interactive AI assistant with free Gemini API
ā āāā gemini_fitness_assistant.py # Main client script
ā āāā requirements.txt # Python dependencies
ā āāā .env.example # Environment configuration template
ā āāā quick_start.sh # Automated setup script
ā āāā README.md # Detailed documentation
ā
āāā agents/
ā āāā agent_discovery/ # Agent card discovery & capability negotiation
ā ā āāā src/main.rs # Discovery client implementation
ā ā āāā Cargo.toml # Dependencies
ā ā āāā README.md # Detailed documentation
ā ā
ā āāā task_manager/ # Task lifecycle management
ā ā āāā src/main.rs # Task polling and monitoring
ā ā āāā Cargo.toml # Dependencies
ā ā āāā README.md # Detailed documentation
ā ā
ā āāā fitness_analyzer/ # Production autonomous agent
ā āāā src/
ā ā āāā main.rs # Entry point
ā ā āāā a2a_client.rs # A2A protocol client
ā ā āāā analyzer.rs # Fitness analysis logic
ā ā āāā scheduler.rs # Autonomous scheduling
ā ā āāā config.rs # Configuration management
ā āāā tests/ # Unit and integration tests
ā āāā run.sh # Helper script
ā āāā Cargo.toml # Dependencies
ā āāā README.md # Detailed documentation
ā
āāā README.md # This file
```
---
## A2A Protocol Architecture
### Core Components
```
āāāāāāāāāāāāāāā āāāāāāāāāāāāāāā
ā Agent A ā ā Agent B ā
ā (Client) ā ā (Remote) ā
āāāāāāāā¬āāāāāāā āāāāāāāā¬āāāāāāā
ā ā
ā 1. GET /a2a/agent-card ā
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā>ā
ā 2. Agent Card (capabilities) ā
ā<āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¤
ā ā
ā 3. POST /a2a/auth ā
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā>ā
ā 4. Session Token ā
ā<āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¤
ā ā
ā 5. POST /a2a/execute (tool) ā
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā>ā
ā 6. Tool Result ā
ā<āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¤
ā ā
ā 7. POST /a2a/execute (task) ā
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā>ā
ā 8. Task ID ā
ā<āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¤
ā ā
ā 9. Poll task status ā
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā>ā
ā 10. Task status + result ā
ā<āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¤
```
---
## Key A2A Concepts
### 1. Agent Cards
Agent cards are JSON documents describing an agent's capabilities, similar to OpenAPI specs for REST APIs.
**What's in an agent card:**
- Agent name, version, description
- Available capabilities (e.g., "fitness-data-analysis")
- Tool definitions with input/output schemas
- Authentication methods (OAuth2, API Key)
- Rate limits, contact info, metadata
**Why it matters**: Agents discover each other's capabilities dynamically, avoiding hard-coded assumptions.
### 2. Task Management
A2A supports both synchronous and asynchronous task execution.
**Task Lifecycle:**
```
pending ā running ā completed
ā failed
ā cancelled
```
**When to use tasks:**
- Operations taking > 30 seconds
- Batch processing
- Scheduled jobs
- Operations that may fail and need retry logic
### 3. JSON-RPC 2.0
A2A uses JSON-RPC 2.0 over HTTP(S) as the transport protocol.
**Standard methods:**
- `a2a/initialize` - Protocol handshake
- `tools/list` - Get available tools
- `tools/call` - Execute a tool
- `tasks/create` - Create long-running task
- `tasks/get` - Query task status
- `tasks/list` - List all tasks
- `tasks/cancel` - Cancel a task
### 4. Authentication
A2A supports multiple authentication schemes:
| Method | Use Case | Example |
|--------|----------|---------|
| **Client Credentials** | Service-to-service | Automated agents |
| **OAuth2** | User-delegated access | User authorizes agent to access their data |
| **API Key** | Simple service auth | Quick integrations |
---
## Specification Compliance
### A2A Compliance
Pierre's A2A implementation follows the [official A2A specification](https://github.com/google/A2A):
- ā
Agent cards with capability discovery
- ā
JSON-RPC 2.0 over HTTP(S)
- ā
Client credentials authentication
- ā
OAuth2 authentication support
- ā
Task lifecycle management (create, get, list, cancel)
- ā
Tool execution with schemas
- ā
Error handling with standard codes
- ā ļø Server-Sent Events (SSE) - acknowledged as not supported (stateless design preference)
- ā ļø Webhooks - configured but not yet active
### MCP Compliance
Pierre's MCP implementation follows the [official MCP specification](https://spec.modelcontextprotocol.io/):
- ā
HTTP JSON-RPC transport
- ā
Tool discovery and execution
- ā
Resource management
- ā
Prompt templates
- ā
Sampling (bidirectional LLM requests)
- ā
Argument completion
- ā
Progress notifications
- ā
Cancellation support
---
## Learn More
- **A2A Specification**: [github.com/google/A2A](https://github.com/google/A2A)
- **Pierre A2A Documentation**: [docs/tutorial/chapter-18-a2a-protocol.md](../docs/tutorial/chapter-18-a2a-protocol.md)
- **MCP Specification**: [modelcontextprotocol.io](https://modelcontextprotocol.io)
- **Pierre MCP Documentation**: [docs/tutorial/chapter-01-introduction.md](../docs/tutorial/chapter-01-introduction.md)
---
## Troubleshooting
### MCP Issues
**"Error: google-generativeai package not installed"**
```bash
pip install -r requirements.txt
```
**"ā Login failed: Connection refused"**
- Ensure Pierre server is running: `cargo run --bin pierre-mcp-server`
- Check server is accessible at http://localhost:8081
### A2A Issues
**"Authentication failed"**
- Make sure you've registered an A2A client
- Check that `PIERRE_A2A_CLIENT_ID` and `PIERRE_A2A_CLIENT_SECRET` are set correctly
- Verify Pierre server is running
**"No activities found"**
- Connect a Strava or Fitbit account via Pierre web UI
- Ensure OAuth connection is active
- Check server logs for provider API errors
**"Agent card fetch failed"**
- Verify Pierre server is running on the expected URL
- Check `PIERRE_SERVER_URL` environment variable
- Ensure `/a2a/agent-card` endpoint is accessible
---
## Contributing
Found a bug or want to add a new example? Contributions welcome!
1. Examples should demonstrate real-world usage patterns
2. Include comprehensive README with "What it demonstrates" section
3. Follow language best practices (Rust/Python) and Pierre coding standards
4. Add tests for new functionality
5. MCP examples should work with free/open-source LLMs when possible
---
## License
All examples are licensed under either:
- Apache License, Version 2.0
- MIT License
at your option.
