Google Cloud Logging MCP Server - A Model Context Protocol server for accessing Google Cloud logs
Google Cloud Logging MCP Server - A Model Context Protocol server for accessing Google Cloud logs. The cloud logging mcp mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 1 defined tool rather than through you.
A Model Context Protocol (MCP) server that provides access to Google Cloud Logging. This server allows AI assistants to query, search, and analyze logs from Google Cloud Platform projects.
Everything the assistant can do here goes through one of these:
Prerequisites — The Prerequisites tool exposed by this serverYou will need 2 environment variables: GOOGLE_CLOUD_PROJECT, GOOGLE_APPLICATION_CREDENTIALS. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Plenty of cloud and infrastructure servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Cloud Logging MCP's toolset — Prerequisites — is a fair guide to whether it matches your workflow. It is maintained by swen128; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Prerequisites | The Prerequisites tool exposed by this server. |
| Variable | Description | Required |
|---|---|---|
| GOOGLE_CLOUD_PROJECT | Configuration value read at startup. | Optional |
| GOOGLE_APPLICATION_CREDENTIALS | Configuration value read at startup. | Optional |
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Stop letting your assistant hallucinate n8n node parameters — this server hands it the real schemas, templates and validation.
AWS Labs' official server suite — current AWS docs, CDK guidance, cost analysis and service tools.
Cloud browsers for AI agents — automation sessions that run in Browserbase's fleet, not on your machine.
Workers, KV, R2 and D1 by conversation — Cloudflare's official remote servers for building and observability.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.