kitagry/gcp-telemetry-mcp
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A Model Context Protocol (MCP) server for Google Cloud Platform telemetry services, providing seamless integration with GCP observability tools.
The GCP Telemetry MCP Server is designed to facilitate the integration of Google Cloud Platform's observability tools, such as Cloud Logging, Cloud Monitoring, Cloud Trace, and Cloud Profiler, into a cohesive telemetry service. This server allows users to write, list, and manage log entries, create and query custom metrics, analyze distributed traces, and profile applications with ease. By leveraging the power of Google Cloud's APIs, the server provides a robust solution for monitoring and analyzing application performance and behavior. It supports advanced features like structured logging, custom metric descriptors, time series data management, trace span updates, and profiling sessions, making it an essential tool for developers and operations teams looking to enhance their cloud-based applications' observability.
Features
- Structured logging with multiple severity levels and custom labels.
- Custom metric descriptor creation and time series data management.
- Advanced trace analysis with filtering, pagination, and span updates.
- Profiling sessions for various profile types including CPU and HEAP.
- Seamless integration with Google Cloud's authentication and APIs.
Usages
usage with local integration stdio
python mcp.run(transport='stdio') # Tools defined via @mcp.tool() decorator
usage with local integration subprocess
python command='uv', args=['run', 'server.py'] # Launch using virtual environment
usage with remote integration sse
python mcp.run(transport='sse', host="0.0.0.0", port=8000) # Specify SSE endpoint
usage with remote integration streamable http
yaml paths: /mcp: post: x-ms-agentic-protocol: mcp-streamable-1.0 # Copilot Studio integration
usage with platform integration github
{"command": "docker", "args": ["run", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"]}
usage with platform integration fastmcp
python from mcp.server import FastMCP app = FastMCP('demo') @app.tool() async def query(): ...
Tools
write_log_entry
Write a log entry to Cloud Logging with structured data and custom labels.
list_log_entries
List log entries from Cloud Logging with filtering and pagination.
create_metric_descriptor
Create a custom metric descriptor in Cloud Monitoring.
write_time_series
Write time series data to Cloud Monitoring.
list_time_series
List time series data from Cloud Monitoring with advanced filtering.