"Projects that showcase advanced GitHub repository search techniques" matching MCP connectors:

Read-only Amazon SES observability: search events, inspect bounces, pull delivery stats.

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.

Mezmo MCP is a remote Model Context Protocol (MCP) server that lets AI assistants and IDE chat agents interact with the Mezmo observability platform via the Model Context Protocol. Use it for streamlined observability, log analysis, and root-cause analysis in your favorite tools. Add Mezmo MCP and you can: 🕵️ Run advanced Root-cause analysis over recent logs 📦 List and describe Pipelines 📤 Export and filter Logs with powerful query syntax

heera.it via Agentimus: AI readiness, traffic, request log, search & index reports, by approval.

Live service status, active incidents, search, and outage history from Downtester.

MCP control plane for AI developers managing customer mail, DNS, projects, keys, and diagnostics.

Read-only performance insights for your Boosthis projects: speed, crashes, traces, and fixes.

API and website monitoring that validates the JSON a response returns, not just its status code. Create endpoint checks, pull incident reports carrying the actual failed payload, and read health scores from your editor.

Open-source agent that observes and fixes your application. Query logs, traces, metrics, incidents.

MCP-native AI SRE: ask what's broken in production, get a reviewed GitHub fix PR.

Read-only access to Auralogs production logs: search logs, inspect errors, review AI analyses.

MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.

Cloud hosted Okahu MCP server that helps you manage genAI trace data