"Projects that showcase advanced GitHub repository search techniques" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Rudow's mcp server lets your AI assistant — Claude, Cursor, Windsurf, VS Code and others — query your Rudow projects directly: logs, metrics, incidents and semantic search.
Deploy apps from GitHub, read build/runtime logs, and manage your app's users from your AI agent.
Vigilia watches your live app, explains every problem in plain language and writes the fix prompt. Your AI fixes it; Vigilia confirms with real traffic. Vigilia watches web apps that are live with real users, including apps built with Lovable, Bolt, v0, Cursor and Claude Code. It catches errors, failed requests and downtime, explains each problem in plain language, and writes a fix prompt for it. Connect Vigilia to Claude and ask what's broken. Claude lists your open incidents worst first. For each one it reads the plain-language diagnosis, who it affects, the technical evidence (error, page, failing request, stack frames and what the visitor did right before) and Vigilia's fix prompt. In Claude Code or another coding agent, Claude can then fix the cause in your own codebase. Once you've deployed, it marks the incident fixed, and Vigilia watches real traffic to confirm the error stayed away. If it comes back, the incident reopens as a regression. You can also ask how an app is doing (uptime, open incidents, today's traffic), get the exact install snippet for a new project, and check that a live site is running Vigilia. Vigilia never reads or changes your code: the fixing happens in your session, by your AI, in front of you. The only tool that changes anything is marking an incident fixed (the install check just records its result on the app), and you can connect with read-only access. Your data is stored in the EU. Emails, tokens and card numbers are stripped from what visitors' browsers send, and only that scrubbed error context is sent to Anthropic to write the diagnosis. Requires a Vigilia account. The free plan covers one app; reporting backend errors (Supabase, Next.js routes, Workers) is part of Pro.
Explore GitHub Actions job performance, compute costs and failures with Runzumi analytics.
Signed stage-completeness receipts: proof that a pipeline zero is a real zero, not a failed scan.
DevOps, SRE, and QA intelligence for Cursor — investigate bugs, assess release readiness, search logs, triage outages, diagnose failed CI, check deployments, and query connected cloud platforms. Secure remote MCP with OAuth2 and read-only QA subagents.
Hear it from nimo, not your customers. Monitoring that tells you the fix, not just the alert: uptime, Core Web Vitals, cron check-ins, plus Journey Watch for your signup and checkout flows. Free one-off checks, no account needed.
Is GitHub, npm, Cloudflare or your AI provider down right now? 20 status pages, one call.
Read-only Amazon SES observability: search events, inspect bounces, pull delivery stats.
Downdetector for AI agents: stop paying for retries that can't succeed. Check once, then report.
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.
Ingest and search LogsLoom logs from coding agents.
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.
Read Airbrake projects, error groups, notices, deploys and stats; mute or unmute error groups.
Correlate GitHub releases with Sentry issues and PagerDuty incidents.
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.
Oviond brings data from 100+ marketing platforms into one reporting platform. Through the Oviond MCP server, AI assistants can securely access and work with Oviond clients, projects, reports, dashboards, widgets, and marketing data. Ask questions about your reporting data, analyze marketing performance, and manage reporting workflows directly through your AI assistant.
heera.it via Agentimus: AI readiness, traffic, request log, search & index reports, by approval.
MCP-native AI SRE: ask what's broken in production, get a reviewed GitHub fix PR.
