"Minecraft Protocol Servers Written in Python" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Read your team's hosted journal from an AI agent: every machine's streams, in hub order.
Observed facts on public MCP servers: protocol checks, tool changes, signed evidence. No verdicts.
Uptime and website monitoring for AI agents. Query monitor status, incidents, heartbeats, domain expiry, and status pages in your Vantaj Uptime workspace.
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.
Free anonymous website, DNS, email and TLS checks, plus monitor read and opt-in write access.
Free spend report from an agent log, no key. Hosted proxies: caps, audit export, buy calls.
Know when cron jobs and AI agents stop running: create monitors and check in from your agent.
MCP tool observatory: do registry servers answer, and are their answers true? No key.
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
MCP-native AI SRE: ask what's broken in production, get a reviewed GitHub fix PR.
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.
Analytics for MCP servers. Query your tool calls, first-call success, retries and schema cost.
Analytics for MCP servers. Find out which of your tools agents get wrong. MCPulse shows you which tools AI agents retry, which come back empty, and which they never call at all. Two lines inside your own server. It never sees your arguments or your results. getmcpulse.com
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
- aictrlOAuthdev.aictrl
Governance and grounding layer for engineering teams running AI coding agents (Claude Code, Cursor, Codex). Grounds agents in your codebase's knowledge graph, and adds session audit, policy controls and cost/token visibility.
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.
Proxy Gemini (Vertex AI) completions wrapped in OpenTelemetry trace spans; returns the answer plus t
- gtmetrixOAuthcom.gtmetrix
Analyze web performance and get optimization insights from GTmetrix, directly in your AI workflow.
Monitor services, manage incidents and status pages in Statuser.cloud from your AI assistant.
MonoDuty MCP connects compatible assistants to uptime monitoring, job heartbeats and webhook alerts in an on-call workspace. Inspect monitoring coverage, prepare proposals, and create or manage supported resources with separate permissions. Hosted Streamable HTTP endpoint with OAuth. Requires an active verified MonoDuty account, workspace admin access and an API-eligible plan. Docs: https://monoduty.com/docs/mcp
