"Generating Answers Using Google Search" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Read-only Amazon SES observability: search events, inspect bounces, pull delivery stats.
Read-only WooCommerce checkout and revenue incident diagnosis using privacy-safe store signals and public release evidence.
Cookieless web analytics your coding agent reads: traffic answers, deploy impact, what broke. One connection covers every site on the account.
Ingest and search LogsLoom logs from coding agents.
Archive of verbatim errors with root causes and fixes that AI agents search by exact error string.
Search + patterns, maturity assessment, context pricing, redaction checks - a practice as tools.
Cookieless web analytics your coding agent reads: traffic answers, deploy impact, what broke.
MCP tool observatory: do registry servers answer, and are their answers true? No key.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.
heera.it via Agentimus: AI readiness, traffic, request log, search & index reports, by approval.
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
Connect engineering metrics, DORA performance, and deploy risk scoring to any AI assistant. Score PRs for deployment risk using a 36-signal model, query team health, incidents, coverage, and more.
Search log events, investigate anomalies, and manage cases in your Knowledge Grid tenant.
The Buildkite MCP server exposes Buildkite product data (pipelines, builds, jobs, and test data) to AI tools, editors, and agents through the Model Context Protocol. It provides capabilities including pipeline creation and management, build monitoring with specialized tools like 'wait_for_build', efficient log querying using Apache Parquet conversion and caching, and OAuth-based authentication for both read-write and read-only access to Buildkite's REST API.
Production observability for AI agents: search runs, read evaluations, acknowledge incidents.
Live service status, active incidents, search, and outage history from Downtester.
Read-only access to Auralogs production logs: search logs, inspect errors, review AI analyses.
- ShipbookOAuthio.shipbook
Debug production issues using Shipbook logs and Loglytics error insights.
