"How to allow an LLM to ask questions to a user" matching MCP connectors:

Fixter's MCP provides a stream-lined agentic way to onboard, setup and use the Fixter monitoring and observability platform. Check out more at https://fixter.dev/

AI/LLM agent output audit MCP: policy eval, tamper-evident chain, AI safety, x402 USDC on Base.

Free anonymous website, DNS, email and TLS checks, plus read-only access to your monitors.

Free MCP window into a live autonomous machine-economy experiment: telemetry, hypothesis scoreboard.

Uptime watchdog and dead man switch for AI agents and cron jobs. Alerts you when a job goes silent.

EU AI Act Art-14 runtime oversight: allow / flag / gate-to-human on an agent action, with receipt.

Live health and AI-readable metadata of invokera.com. Demo of an Invokera-hosted MCP server.

An inter-agent graffiti wall for one completely optional trace.

Measured latency & uptime for AI inference APIs, by region. Exposes a get_ai_api_latency tool.

Public MCP digital twin with synthetic systems and an agent firewall. No customer data.

Live reliability for AI agent tools: is it working right now, and how do I call it correctly?

Read-only MCP access to sessions, funnels, campaigns, errors, live visitors, and anomalies.

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.

Cloudflare Workers MCP server: llm-output-quality-monitor

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.

The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.

Read-only cloud cost and infrastructure governance across AWS, Azure and GCP. 85 tools covering cost overview and trends, cost by provider/resource/tag/team, budgets, resources, schedules, recommendations, tagging policies, audit logs, anomalies, Kubernetes resources and pod logs. Hosted remote server, nothing to install. Docs: https://zop.dev/learn/mcp-server?utm_source=glama&utm_medium=listing&utm_campaign=mcp-directory Claude setup: https://zop.dev/learn/how-to/set-up-zopnight-mcp-for-claude

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

Proxy Gemini (Vertex AI) completions wrapped in OpenTelemetry trace spans; returns the answer plus t

Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith