MCP (Model Context Protocol)

pip install "sentry-sdk[mcp]" 

import sentry_sdk  sentry_sdk.init(  dsn="https://examplePublicKey@o0.ingest.sentry.io/0",  # Add data like request headers and IP for users, if applicable;  # see https://docs.sentry.io/platforms/python/data-management/data-collected/ for more info  send_default_pii=True,  #  performance  # Set traces_sample_rate to 1.0 to capture 100%  # of transactions for tracing.  traces_sample_rate=1.0,  #  performance  #  profiling  # To collect profiles for all profile sessions,  # set `profile_session_sample_rate` to 1.0.  profile_session_sample_rate=1.0,  # Profiles will be automatically collected while  # there is an active span.  profile_lifecycle="trace",  #  profiling  #  logs   # Enable logs to be sent to Sentry  enable_logs=True,  #  logs ) 

import sentry_sdk from sentry_sdk.integrations.mcp import MCPIntegration  from mcp.server.fastmcp import FastMCP  # Initialize Sentry sentry_sdk.init(  dsn="https://examplePublicKey@o0.ingest.sentry.io/0",  traces_sample_rate=1.0,  # Add data like tool inputs/outputs;  # see https://docs.sentry.io/platforms/python/data-management/data-collected/ for more info  send_default_pii=True, )  # Create the MCP server mcp = FastMCP("Example MCP Server")  # Define a tool @mcp.tool() async def calculate_sum(a: int, b: int) -> int:  """Add two numbers together."""  return a + b  @mcp.tool() def greet_user(name: str) -> str:  """Generate a personalized greeting."""  return f"Hello, {name}! Welcome to the MCP server."  # Define a resource @mcp.resource("config://settings") def get_settings() -> str:  """Get server configuration settings."""  return "Server Configuration: Version 1.0.0"  # Define a prompt @mcp.prompt() def code_review_prompt(language: str = "python") -> str:  """Generate a code review prompt for a specific language."""  return f"You are an expert {language} code reviewer..."  # Run the server mcp.run() 

import asyncio from typing import Any  import sentry_sdk from sentry_sdk.integrations.mcp import MCPIntegration  from mcp.server.lowlevel import Server from mcp.server import stdio from mcp.types import Tool, TextContent, GetPromptResult, PromptMessage  # Initialize Sentry sentry_sdk.init(  dsn="https://examplePublicKey@o0.ingest.sentry.io/0",  traces_sample_rate=1.0,  send_default_pii=True, )  # Create the low-level MCP server server = Server("example-lowlevel-server")  # List available tools @server.list_tools() async def list_tools() -> list[Tool]:  return [  Tool(  name="calculate_sum",  description="Add two numbers together",  inputSchema={  "type": "object",  "properties": {  "a": {"type": "number", "description": "First number"},  "b": {"type": "number", "description": "Second number"},  },  "required": ["a", "b"],  },  ),  ]  # Handle tool execution @server.call_tool() async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:  if name == "calculate_sum":  a = arguments.get("a", 0)  b = arguments.get("b", 0)  result = a + b  return [TextContent(type="text", text=f"The sum is {result}")]   return [TextContent(type="text", text=f"Unknown tool: {name}")]  async def main():  async with stdio.stdio_server() as (read_stream, write_stream):  await server.run(  read_stream,  write_stream,  server.create_initialization_options(),  )  if __name__ == "__main__":  asyncio.run(main()) 

import sentry_sdk from sentry_sdk.integrations.mcp import MCPIntegration  sentry_sdk.init(  # ...  # Add data like tool inputs and outputs;  # see https://docs.sentry.io/platforms/python/data-management/data-collected/ for more info  send_default_pii=True,  integrations=[  MCPIntegration(  include_prompts=False, # Tool and prompt inputs/outputs will not be sent to Sentry  ),  ], )