Feat/add logfire

2025-05-16-fastapi-demo/README.md

@@ -1,9 +1,10 @@
-# FastAPI Demo with Math, Database and PydanticAI
+# FastAPI Demo with Math, Database, PydanticAI and MCP
 
 This is a FastAPI application that demonstrates:
 - Mathematical operations (division, Fibonacci)
 - Database operations with SQLAlchemy
 - PydanticAI agent integration with Tavily search
+- MCP (Model Context Protocol) integration with Playwright MCP server
 - Logfire observability
 
 ## Setup
@@ -13,15 +14,20 @@ This is a FastAPI application that demonstrates:
    uv sync
    ```
 
-2. Create a `.env` file in the root directory with the following environment variables:
+2. Ensure you have Node.js installed for the MCP filesystem server:
+   ```bash
+   node --version  # Should be v16 or higher
+   ```
+
+3. Create a `.env` file in the root directory with the following environment variables:
    ```
    OPENAI_API_KEY=your_openai_api_key_here
    TAVILY_API_KEY=your_tavily_api_key_here
    LOGFIRE_TOKEN=your_logfire_token_here
    DATABASE_URL=sqlite:///./test.db
    ```
 
-3. Run the application:
+4. Run the application:
    ```bash
    uv run uvicorn src.app:app --host 0.0.0.0 --port 8000 --reload
    ```
@@ -34,6 +40,7 @@ This is a FastAPI application that demonstrates:
 - `GET /items/` - List all items with pagination
 - `GET /items/{item_id}` - Get a specific item by ID
 - `POST /agent/query` - Query the PydanticAI agent with a question
+- `POST /mcp/query` - Query the MCP-enabled agent with Playwright MCP
 
 ## Example Usage
 
@@ -44,6 +51,13 @@ curl -X POST "http://localhost:8000/agent/query" \
      -d '{"question": "How do I use PydanticAI tools?"}'
 ```
 
+Query the MCP agent with filesystem capabilities:
+```bash
+curl -X POST "http://localhost:8000/mcp/query" \
+     -H "Content-Type: application/json" \
+     -d '{"question": "Create a simple Python script that prints hello world and save it to hello.py"}'
+```
+
 Run evals:
 ```bash
 PYTHONPATH=. uv run python tests/evals.py

2025-05-16-fastapi-demo/pyproject.toml

@@ -11,8 +11,10 @@ dependencies = [
     "uvicorn>=0.34.2",
     "pydantic-ai>=0.2.9",
     "pydantic-ai-slim[tavily]>=0.2.9",
+    "mcp>=1.9.2",
     "python-dotenv>=1.1.0",
     "pydantic-evals>=0.2.9",
+    "logfire-mcp>=0.0.11",
 ]
 
 [tool.ruff]

2025-05-16-fastapi-demo/src/app.py

@@ -9,6 +9,7 @@
 from sqlalchemy.orm import sessionmaker, Session
 
 from src.agent import build_agent, answer_question, BotResponse
+from src.mcp_agent import answer_mcp_question, MCPBotResponse
 
 logfire.configure(
     service_name='api',
@@ -26,6 +27,7 @@
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 Base = declarative_base()
 logfire.instrument_sqlalchemy(engine)
+logfire.instrument_mcp()
 
 
 # Database model
@@ -58,6 +60,10 @@ class AgentQuery(BaseModel):
     question: str
 
 
+class MCPQuery(BaseModel):
+    question: str
+
+
 # Dependency to get DB session
 def get_db():
     db = SessionLocal()
@@ -146,6 +152,17 @@ async def query_agent(query: AgentQuery, agent: Agent[None, BotResponse] = Depen
     return response
 
 
+@app.post("/mcp/query", response_model=MCPBotResponse)
+async def query_mcp_agent(query: MCPQuery):
+    """
+    Queries the MCP-enabled PydanticAI agent with browser automation capabilities.
+    """
+    logfire.info(f"Querying MCP agent with question: {query.question}")
+    response = await answer_mcp_question(query.question)
+    return response
+
+
+
 if __name__ == '__main__':  # Fixed double asterisks
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)  # Added this line to complete the if block

2025-05-16-fastapi-demo/src/mcp_agent.py

@@ -0,0 +1,83 @@
+import asyncio
+from pathlib import Path
+from textwrap import dedent
+from typing import Annotated
+
+import logfire
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent
+from pydantic_ai.mcp import MCPServerStdio
+
+ROOT_DIR = Path(__file__).parent.parent
+load_dotenv(dotenv_path=ROOT_DIR / ".env")
+
+# Configure logfire instrumentation
+logfire.configure(scrubbing=False, service_name='playwright-browser')
+logfire.instrument_mcp()
+logfire.instrument_pydantic_ai()
+
+class MCPBotResponse(BaseModel):
+    answer: str
+    reasoning: str
+    services_used: list[str] = []
+    confidence_percentage: Annotated[int, Field(ge=0, le=100)]
+
+SYSTEM_PROMPT = dedent(
+    """
+    You're a helpful AI assistant with access to browser automation and Logfire telemetry analysis capabilities.
+
+    Browser capabilities (via Playwright):
+    - Navigate to websites, interact with web pages, take screenshots, and extract information
+    - Be thorough in web navigation and information extraction
+    - Take screenshots when helpful for verification
+    - Extract relevant information clearly and accurately
+
+    Logfire capabilities:
+    - Find and analyze exceptions in OpenTelemetry traces grouped by file
+    - Get detailed trace information about exceptions in specific files
+    - Run custom SQL queries on traces and metrics data
+    - Access OpenTelemetry schema information for query building
+    - Analyze application performance and error patterns
+
+    When working with these services:
+    - Explain what you're doing clearly
+    - Be mindful of website terms of service and respectful browsing practices
+    - Use appropriate time ranges for telemetry queries (max 7 days)
+    - Help identify patterns in application behavior and errors
+
+    Give a confidence percentage for your answer, from 0 to 100.
+    List any services you used (e.g., "playwright", "logfire") in the services_used field.
+    """
+)
+
+# Set up MCP servers with correct command syntax
+browser_mcp = MCPServerStdio('npx', args=['--yes', '@playwright/mcp@latest'], tool_prefix='browser')
+logfire_mcp = MCPServerStdio('uvx', args=['logfire-mcp'], tool_prefix='logfire')
+
+# Create the agent with both MCP servers
+agent = Agent(
+    'openai:gpt-4o',
+    output_type=MCPBotResponse,
+    system_prompt=SYSTEM_PROMPT,
+    mcp_servers=[browser_mcp, logfire_mcp],
+    instrument=True,
+)
+
+async def answer_mcp_question(question: str) -> MCPBotResponse:
+    """Run a question through the MCP-enabled agent."""
+    async with agent.run_mcp_servers():
+        result = await agent.run(user_prompt=question)
+        return result.output
+
+async def main():
+    """Example usage of the browser and Logfire telemetry agent."""
+    question = ('Help me analyze my application: First, check for any exceptions in traces from the last hour using Logfire. '
+               'Then navigate to the Logfire documentation to get information about best practices for error monitoring. '
+               'Finally, provide recommendations based on what you find.')
+
+    result = await answer_mcp_question(question)
+    print(result)
+
+if __name__ == '__main__':
+    asyncio.run(main())