"Information on RAG Documents or Processing" matching MCP connectors:

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

Live status for 172 cloud and SaaS vendors from their official feeds. Is it you, or is it them?

Uptime, API and server monitoring with outages, reporting, on-call and status pages.

Read Spike.sh incidents, on-call, escalations and services; acknowledge, resolve, set priority.

Manage incidents and on-call: list/create/update incidents, who is on call, on-call overrides.

Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.

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.

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

Manage cron/heartbeat checks, read pings and flips, pause/resume/delete on Healthchecks.io.

Read incidents, services, teams, on-call schedules; acknowledge, resolve and note incidents.

Draw your app's architecture on a live canvas and flag the bottlenecks and security gaps.

Uptime, SSL, DNS and domain monitoring you can talk to from Claude or any MCP client.

Mobile observability for AI agents. Investigate crashes, hangs, ANRs, bugs, and app performance, and triage app store reviews, directly from your IDE or terminal.

Log, evaluate, and ground AI decisions against authority context. Returns PASS, WARN, or BLOCK.

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.

The Polar Signals MCP server enables AI assistants to connect directly with performance profiling data, allowing users to analyze application performance through natural language queries. Key capabilities include querying CPU performance and memory usage, exploring profiling metadata like profile types and labels, and providing AI-driven code optimization suggestions directly within development environments like Claude Code or Cursor.